Wei, Y.C.[Yun-Chao],
Liang, X.D.[Xiao-Dan],
Chen, Y.P.[Yun-Peng],
Jie, Z.Q.[Ze-Qun],
Xiao, Y.H.[Yan-Hui],
Zhao, Y.[Yao],
Yan, S.C.[Shui-Cheng],
Learning to segment with image-level annotations,
PR(59), No. 1, 2016, pp. 234-244.
Elsevier DOI
1609
Semantic segmentation. with CNNs
BibRef
Holliday, A.[Andrew],
Barekatain, M.[Mohammadamin],
Laurmaa, J.[Johannes],
Kandaswamy, C.[Chetak],
Prendinger, H.[Helmut],
Speedup of deep learning ensembles for semantic segmentation using a
model compression technique,
CVIU(164), No. 1, 2017, pp. 16-26.
Elsevier DOI
1801
Semantic segmentation
BibRef
Liu, Y.[Yu],
Nguyen, D.M.[Duc Minh],
Deligiannis, N.[Nikos],
Ding, W.R.[Wen-Rui],
Munteanu, A.[Adrian],
Hourglass-Shape Network Based Semantic Segmentation for High
Resolution Aerial Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Abrahamyan, L.[Lusine],
Deligiannis, N.[Nikos],
Entropy-Based Feature Extraction for Real-Time Semantic Segmentation,
ICIP22(591-595)
IEEE DOI
2211
Computational modeling, Semantics, Benchmark testing,
Feature extraction, Entropy, Real-time systems,
neural network
BibRef
Dong, L.[Le],
Feng, N.[Ning],
Zhang, Q.[Qianni],
LSI: Latent semantic inference for natural image segmentation,
PR(59), No. 1, 2016, pp. 282-291.
Elsevier DOI
1609
Image Segmentation
BibRef
Lu, Z.W.[Zhi-Wu],
Fu, Z.Y.[Zhen-Yong],
Xiang, T.[Tao],
Han, P.[Peng],
Wang, L.W.[Li-Wei],
Gao, X.[Xin],
Learning from Weak and Noisy Labels for Semantic Segmentation,
PAMI(39), No. 3, March 2017, pp. 486-500.
IEEE DOI
1702
Computational modeling
BibRef
Xu, N.[Nuo],
Huo, C.L.[Chun-Lei],
Learning Deep Relationship for Object Detection,
IEICE(E101-D), No. 1, January 2018, pp. 273-276.
WWW Link.
1801
BibRef
Guo, Y.M.[Yan-Ming],
Liu, Y.[Yu],
Georgiou, T.[Theodoros],
Lew, M.S.[Michael S.],
A review of semantic segmentation using deep neural networks,
MultInfoRetr(8), No. 2, June 2018, pp. 87-93.
Springer DOI
1805
Survey, Semantic Segmentation.
BibRef
Li, A.[Aoxue],
Lu, Z.W.[Zhi-Wu],
Wang, L.W.[Li-Wei],
Han, P.[Peng],
Wen, J.R.[Ji-Rong],
Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation,
Cyber(48), No. 1, January 2018, pp. 253-263.
IEEE DOI
1801
Image segmentation, Matrix decomposition, Noise measurement,
Noise reduction, Semantics, Symmetric matrices, Visualization,
semantic segmentation
BibRef
Cao, Y.,
Shen, C.,
Shen, H.T.,
Exploiting Depth From Single Monocular Images for Object Detection
and Semantic Segmentation,
IP(26), No. 2, February 2017, pp. 836-846.
IEEE DOI
1702
estimation theory
BibRef
Zhang, M.[Mi],
Hu, X.Y.[Xiang-Yun],
Zhao, L.[Like],
Lv, Y.[Ye],
Luo, M.[Min],
Pang, S.Y.[Shi-Yan],
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation
of High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Chen, T.,
Lin, L.,
Wu, X.,
Xiao, N.,
Luo, X.,
Learning to Segment Object Candidates via Recursive Neural Networks,
IP(27), No. 12, December 2018, pp. 5827-5839.
IEEE DOI
1810
Proposals, Merging, Semantics, Feature extraction, Neural networks,
Measurement, Image segmentation, Object proposal generation,
deep learning
BibRef
Wang, W.G.[Wen-Guan],
Zhao, S.Y.[Shu-Yang],
Shen, J.B.[Jian-Bing],
Hoi, S.C.H.[Steven C. H.],
Borji, A.[Ali],
Deeply Supervised Salient Object Detection with Short Connections,
PAMI(41), No. 4, April 2019, pp. 815-828.
IEEE DOI
1903
BibRef
And:
Salient Object Detection with Pyramid Attention and Salient Edges,
CVPR19(1448-1457).
IEEE DOI
2002
BibRef
Earlier:
CVPR17(5300-5309)
IEEE DOI
1711
Object detection, Feature extraction, Image edge detection,
Image segmentation, Semantics, Saliency detection,
edge detection.
Computer architecture, Image edge detection, Neural networks.
See also BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network.
BibRef
Ghassemi, S.[Sina],
Fiandrotti, A.[Attilio],
Francini, G.[Gianluca],
Magli, E.[Enrico],
Learning and Adapting Robust Features for Satellite Image
Segmentation on Heterogeneous Data Sets,
GeoRS(57), No. 9, September 2019, pp. 6517-6529.
IEEE DOI
1909
Image segmentation, Satellites, Training, Semantics,
Feature extraction, Labeling, Computer architecture,
satellite image segmentation
BibRef
Zhang, R.M.[Rui-Mao],
Yang, W.[Wei],
Peng, Z.L.[Zhang-Lin],
Wei, P.X.[Peng-Xu],
Wang, X.G.[Xiao-Gang],
Lin, L.[Liang],
Progressively diffused networks for semantic visual parsing,
PR(90), 2019, pp. 78-86.
Elsevier DOI
1903
Visual understanding, Image segmentation,
Recurrent neural networks, Representation learning
BibRef
Li, Y.,
Guo, Y.,
Guo, J.,
Ma, Z.,
Kong, X.,
Liu, Q.,
Joint CRF and Locality-Consistent Dictionary Learning for Semantic
Segmentation,
MultMed(21), No. 4, April 2019, pp. 875-886.
IEEE DOI
1903
Dictionaries, Machine learning, Image segmentation, Semantics,
Task analysis, Inference algorithms, Shape,
locality consistency
BibRef
Masouleh, M.K.[Mehdi Khoshboresh],
Shah-Hosseini, R.[Reza],
Development and evaluation of a deep learning model for real-time
ground vehicle semantic segmentation from UAV-based thermal infrared
imagery,
PandRS(155), 2019, pp. 172-186.
Elsevier DOI
1908
UAV-based thermal infrared imagery, Ground vehicle,
Semantic segmentation, Deep learning,
Gaussian-Bernoulli Restricted Boltzmann Machine
BibRef
Arnab, A.[Anurag],
Zheng, S.[Shuai],
Jayasumana, S.[Sadeep],
Romera-Paredes, B.[Bernardino],
Larsson, M.,
Kirillov, A.,
Savchynskyy, B.,
Rother, C.,
Kahl, F.,
Torr, P.H.S.[Philip H. S.],
Conditional Random Fields Meet Deep Neural Networks for Semantic
Segmentation: Combining Probabilistic Graphical Models with Deep
Learning for Structured Prediction,
SPMag(35), No. 1, January 2018, pp. 37-52.
IEEE DOI
1801
Computational modeling, Feature extraction,
Image segmentation, Semantics, Visualization
BibRef
Brynte, L.[Lucas],
Iglesias, J.P.[José Pedro],
Olsson, C.[Carl],
Kahl, F.[Fredrik],
Learning Structure-From-Motion with Graph Attention Networks,
CVPR24(4808-4817)
IEEE DOI Code:
WWW Link.
2410
Learning systems, Bundle adjustment, Training, Solid modeling,
Runtime, Perturbation methods, Structure-from-motion,
Equivariance
BibRef
Arnab, A.[Anurag],
Jayasumana, S.[Sadeep],
Zheng, S.[Shuai],
Torr, P.H.S.[Philip H. S.],
Higher Order Conditional Random Fields in Deep Neural Networks,
ECCV16(II: 524-540).
Springer DOI
1611
BibRef
Arnab, A.[Anurag],
Torr, P.H.S.[Philip H. S.],
Pixelwise Instance Segmentation with a Dynamically Instantiated
Network,
CVPR17(879-888)
IEEE DOI
1711
BibRef
Earlier:
Bottom-up Instance Segmentation using Deep Higher-Order CRFs,
BMVC16(xx-yy).
HTML Version.
1805
Detectors, Image segmentation, Object detection, Pipelines,
Proposals, Semantics. First, semantic segmentation, then object instance
detection.
BibRef
Zheng, S.[Shuai],
Jayasumana, S.[Sadeep],
Romera-Paredes, B.[Bernardino],
Vineet, V.[Vibhav],
Su, Z.Z.[Zhi-Zhong],
Du, D.L.[Da-Long],
Huang, C.[Chang],
Torr, P.H.S.[Philip H. S.],
Conditional Random Fields as Recurrent Neural Networks,
ICCV15(1529-1537)
IEEE DOI
1602
Combine CNN with CRF.
BibRef
Larsson, M.[Mĺns],
Alvén, J.[Jennifer],
Kahl, F.[Fredrik],
Max-Margin Learning of Deep Structured Models for Semantic Segmentation,
SCIA17(II: 28-40).
Springer DOI
1706
BibRef
Saleh, F.S.[Fatemehsadat S.],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Gould, S.[Stephen],
Incorporating Network Built-in Priors in Weakly-Supervised Semantic
Segmentation,
PAMI(40), No. 6, June 2018, pp. 1382-1396.
IEEE DOI
1805
BibRef
Earlier: A1, A2, A3, A4, A6, A5:
Built-in Foreground/Background Prior for Weakly-Supervised Semantic
Segmentation,
ECCV16(VIII: 413-432).
Springer DOI
1611
Data mining, Image segmentation, Machine learning, Neural networks,
Object recognition, Semantics, Training, Semantic segmentation,
weakly-supervised semantic segmentation
BibRef
Saleh, F.S.[Fatemehsadat S.],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Bringing Background into the Foreground: Making All Classes Equal in
Weakly-Supervised Video Semantic Segmentation,
ICCV17(2125-2135)
IEEE DOI
1802
image classification, image segmentation,
learning (artificial intelligence), video signal processing,
Semantics
BibRef
Lin, G.S.[Guo-Sheng],
Liu, F.[Fayao],
Milan, A.[Anton],
Shen, C.H.[Chun-Hua],
Reid, I.D.[Ian D.],
RefineNet: Multi-Path Refinement Networks for Dense Prediction,
PAMI(42), No. 5, May 2020, pp. 1228-1242.
IEEE DOI
2004
BibRef
Earlier: A1, A3, A4, A5, Only:
RefineNet: Multi-path Refinement Networks for High-Resolution
Semantic Segmentation,
CVPR17(5168-5177)
IEEE DOI
1711
Semantics, Estimation, Image segmentation, Task analysis,
Convolution, Training, Visualization, Convolutional neural network,
dense prediction.
Computer architecture, Image resolution,
Image segmentation, Semantics, Training
BibRef
Chen, B.,
Gong, C.,
Yang, J.,
Importance-Aware Semantic Segmentation for Autonomous Vehicles,
ITS(20), No. 1, January 2019, pp. 137-148.
IEEE DOI
1901
Image segmentation, Autonomous vehicles, Roads, Neural networks,
Feature extraction, Semantics, Reliability, Semantic segmentation,
autonomous driving
BibRef
Fu, K.[Kun],
Lu, W.X.[Wan-Xuan],
Diao, W.H.[Wen-Hui],
Yan, M.L.[Meng-Long],
Sun, H.[Hao],
Zhang, Y.[Yi],
Sun, X.[Xian],
WSF-NET: Weakly Supervised Feature-Fusion Network for Binary
Segmentation in Remote Sensing Image,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
1901
BibRef
Nguyen, T.V.,
Nguyen, K.,
Do, T.,
Semantic Prior Analysis for Salient Object Detection,
IP(28), No. 6, June 2019, pp. 3130-3141.
IEEE DOI
1905
Semantics, Object detection, Image color analysis, Deep learning,
Saliency detection, Task analysis, Visualization,
deep networks
BibRef
Redondo-Cabrera, C.,
Baptista-Ríos, M.,
López-Sastre, R.J.,
Learning to Exploit the Prior Network Knowledge for Weakly Supervised
Semantic Segmentation,
IP(28), No. 7, July 2019, pp. 3649-3661.
IEEE DOI
1906
Image segmentation, Semantics, Training, Task analysis, Data models,
Training data, Tools, Semantic segmentation, weakly supervised, deep learning
BibRef
Guo, D.,
Pei, Y.,
Zheng, K.,
Yu, H.,
Lu, Y.,
Wang, S.,
Degraded Image Semantic Segmentation With Dense-Gram Networks,
IP(29), No. 1, 2020, pp. 782-795.
IEEE DOI
1910
Image segmentation, Semantics, Degradation, Training,
Motion segmentation, Image restoration, Image texture,
degraded images
BibRef
Audebert, N.[Nicolas],
Boulch, A.[Alexandre],
Le Saux, B.[Bertrand],
Lefčvre, S.[Sébastien],
Distance transform regression for spatially-aware deep semantic
segmentation,
CVIU(189), 2019, pp. 102809.
Elsevier DOI
1911
BibRef
Earlier: A1, A3, A4, Only:
Joint Learning from Earth Observation and OpenStreetMap Data to Get
Faster Better Semantic Maps,
EarthVision17(1552-1560)
IEEE DOI
1709
BibRef
Earlier: A1, A3, A4, Only:
Semantic Segmentation of Earth Observation Data Using Multimodal and
Multi-scale Deep Networks,
ACCV16(I: 180-196).
Springer DOI
1704
Buildings, Labeling, Optical imaging, Roads, Semantics, Sensors, Training
BibRef
Zhao, W.,
Hou, X.,
Yu, X.,
He, Y.,
Lu, H.,
Towards Weakly-Supervised Focus Region Detection via Recurrent
Constraint Network,
IP(29), No. , 2020, pp. 1356-1367.
IEEE DOI
1911
Training, Task analysis, Object segmentation, Semantics,
Image segmentation, Dogs, Focus region detection,
box-level supervision
BibRef
Zhang, T.,
Lin, G.,
Cai, J.,
Shen, T.,
Shen, C.,
Kot, A.C.,
Decoupled Spatial Neural Attention for Weakly Supervised Semantic
Segmentation,
MultMed(21), No. 11, November 2019, pp. 2930-2941.
IEEE DOI
1911
Image segmentation, Semantics, Detectors, Training, Task analysis,
Pipelines, Object recognition, Semantic segmentation,
weakly-supervised learning
BibRef
Mi, L.[Li],
Chen, Z.Z.[Zhen-Zhong],
Superpixel-enhanced deep neural forest for remote sensing image
semantic segmentation,
PandRS(159), 2020, pp. 140-152.
Elsevier DOI
1912
BibRef
And:
Corrigendum:
PandRS(168), 2020, pp. 153 - 155.
Elsevier DOI
2009
Neural forest, Superpixel, Remote sensing imagery, Semantic segmentation
BibRef
Wang, Y.D.[Yin-Duo],
Zhang, H.F.[Hao-Feng],
Wang, S.D.[Shi-Dong],
Long, Y.[Yang],
Yang, L.Z.[Long-Zhi],
Semantic combined network for zero-shot scene parsing,
IET-IPR(14), No. 4, 27 March 2020, pp. 757-765.
DOI Link
2003
BibRef
Zhang, Y.[Yang],
David, P.[Philip],
Foroosh, H.[Hassan],
Gong, B.Q.[Bo-Qing],
A Curriculum Domain Adaptation Approach to the Semantic Segmentation
of Urban Scenes,
PAMI(42), No. 8, August 2020, pp. 1823-1841.
IEEE DOI
2007
BibRef
Earlier: A1, A2, A4, Only:
Curriculum Domain Adaptation for Semantic Segmentation of Urban
Scenes,
ICCV17(2039-2049)
IEEE DOI
1802
Semantics, Image segmentation, Task analysis, Adaptation models,
Neural networks, Training, Buildings, Domain adaptation,
self-driving.
computer graphics, convolution, image classification,
learning (artificial intelligence).
BibRef
Zhang, X.,
Wei, Y.,
Yang, Y.,
Huang, T.S.,
SG-One: Similarity Guidance Network for One-Shot Semantic
Segmentation,
Cyber(50), No. 9, September 2020, pp. 3855-3865.
IEEE DOI
2008
Image segmentation, Feature extraction, Testing, Semantics, Training,
Task analysis, Dogs, Few-shot learning, image segmentation,
siamese network
BibRef
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
Yang, L.[Le],
Xx, D.[Dong],
SPFTN: A Joint Learning Framework for Localizing and Segmenting
Objects in Weakly Labeled Videos,
PAMI(42), No. 2, February 2020, pp. 475-489.
IEEE DOI
2001
Videos, Task analysis, Reliability, Supervised learning,
Object segmentation, Semantics, Feature extraction,
self-paced learning
BibRef
Huang, Z.L.[Zi-Long],
Wang, C.Y.[Chun-Yu],
Wang, X.G.[Xing-Gang],
Liu, W.Y.[Wen-Yu],
Wang, J.D.[Jing-Dong],
Semantic Image Segmentation by Scale-Adaptive Networks,
IP(29), 2020, pp. 2066-2077.
IEEE DOI
2001
Image segmentation, Semantics, Detectors, Training, Lips,
Task analysis, Feature extraction, Semantic object parsing,
scale adaptive
BibRef
Huang, Y.,
Tang, Z.,
Chen, D.,
Su, K.,
Chen, C.,
Batching Soft IoU for Training Semantic Segmentation Networks,
SPLetters(27), 2020, pp. 66-70.
IEEE DOI
2001
Training, Integrated circuits, Semantics, Measurement,
Image segmentation, Predictive models, Data models,
semantic segmentation
BibRef
Berman, M.,
Triki, A.R.,
Blaschko, M.B.,
The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization
of the Intersection-Over-Union Measure in Neural Networks,
CVPR18(4413-4421)
IEEE DOI
1812
Indexes, Loss measurement, Optimization, Image segmentation,
Fasteners, Training, Semantics
BibRef
Chai, D.F.[Deng-Feng],
Newsam, S.[Shawn],
Huang, J.F.[Jing-Feng],
Aerial image semantic segmentation using DCNN predicted distance maps,
PandRS(161), 2020, pp. 309-322.
Elsevier DOI
2002
Deep learning, Semantic segmentation, DCNNs, Distance maps, Distance transform
BibRef
Chen, X.,
Lou, X.,
Bai, L.,
Han, J.,
Residual Pyramid Learning for Single-Shot Semantic Segmentation,
ITS(21), No. 7, July 2020, pp. 2990-3000.
IEEE DOI
2007
Semantics, Feature extraction, Task analysis, Decoding, Training,
Image segmentation, Neural networks, Intelligent vehicles,
residual learning
BibRef
Kim, W.,
Kanezaki, A.,
Tanaka, M.,
Unsupervised Learning of Image Segmentation Based on Differentiable
Feature Clustering,
IP(29), 2020, pp. 8055-8068.
IEEE DOI
2008
Image segmentation, Training, Feature extraction, Semantics,
Machine learning, Clustering algorithms, Unsupervised learning,
feature clustering
BibRef
Cermelli, F.[Fabio],
Mancini, M.[Massimiliano],
Rota Buló, S.[Samuel],
Ricci, E.[Elisa],
Caputo, B.[Barbara],
Modeling the Background for Incremental and Weakly-Supervised
Semantic Segmentation,
PAMI(44), No. 12, December 2022, pp. 10099-10113.
IEEE DOI
2212
BibRef
Earlier:
Modeling the Background for Incremental Learning in Semantic
Segmentation,
CVPR20(9230-9239)
IEEE DOI
2008
Semantics, Image segmentation, Annotations, Task analysis, Training,
Automobiles, Standards.
Context modeling.
BibRef
Yang, G.L.[Guang-Lei],
Fini, E.[Enrico],
Xu, D.[Dan],
Rota, P.[Paolo],
Ding, M.L.[Ming-Li],
Nabi, M.[Moin],
Alameda-Pineda, X.[Xavier],
Ricci, E.[Elisa],
Uncertainty-Aware Contrastive Distillation for Incremental Semantic
Segmentation,
PAMI(45), No. 2, February 2023, pp. 2567-2581.
IEEE DOI
2301
Task analysis, Semantics, Image segmentation, Feature extraction,
Training, Uncertainty, Knowledge distillation, semantic segmentation
BibRef
Lai, H.J.[Han-Jiang],
Chen, J.K.[Ji-Kai],
Geng, L.B.[Li-Bing],
Pan, Y.[Yan],
Liang, X.D.[Xiao-Dan],
Yin, J.[Jian],
Improving Deep Binary Embedding Networks by Order-Aware Reweighting
of Triplets,
CirSysVideo(30), No. 4, April 2020, pp. 1162-1172.
IEEE DOI
2004
Binary codes, Training, Hash functions, Image retrieval, Semantics,
Quantization (signal), Dogs, Image retrieval, triplet ranking loss,
nearest neighbor search
BibRef
Lv, F.M.[Feng-Mao],
Liu, H.Y.[Hai-Yang],
Wang, Y.C.[Yi-Chen],
Zhao, J.Y.[Jia-Yi],
Yang, G.W.[Guo-Wu],
Learning Unbiased Zero-Shot Semantic Segmentation Networks Via
Transductive Transfer,
SPLetters(27), 2020, pp. 1640-1644.
IEEE DOI
2010
Semantics, Image segmentation, Neural networks, Visualization,
Training, Predictive models, Machine learning,
zero-shot learning
BibRef
Lv, F.M.[Feng-Mao],
Zhang, J.Y.[Jian-Yang],
Yang, G.W.[Guo-Wu],
Feng, L.[Lei],
Yu, Y.F.[Yu-Feng],
Duan, L.X.[Li-Xin],
Learning Cross-Domain Semantic-Visual Relationships for Transductive
Zero-Shot Learning,
PR(141), 2023, pp. 109591.
Elsevier DOI
2306
Zero-shot learning, Transfer learning, Domain adaptation
BibRef
Zhang, J.Y.[Jian-Yang],
Yang, G.W.[Guo-Wu],
Hu, P.[Ping],
Lin, G.S.[Guo-Sheng],
Lv, F.M.[Feng-Mao],
Semantic Consistent Embedding for Domain Adaptive Zero-Shot Learning,
IP(32), 2023, pp. 4024-4035.
IEEE DOI
2307
Semantics, Prototypes, Entropy, Fans, Knowledge transfer,
Feature extraction, Adaptation models, Zero-shot learning,
transfer learning
BibRef
Guo, R.X.[Rong-Xin],
Sun, X.[Xian],
Chen, K.Q.[Kai-Qiang],
Zhou, X.[Xiao],
Yan, Z.Y.[Zhi-Yuan],
Diao, W.H.[Wen-Hui],
Yan, M.L.[Meng-Long],
JMLNet: Joint Multi-Label Learning Network for Weakly Supervised
Semantic Segmentation in Aerial Images,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Zhou, R.X.[Rui-Xue],
Yuan, Z.Q.[Zhi-Qiang],
Rong, X.[Xuee],
Ma, W.C.[Wei-Cong],
Sun, X.[Xian],
Fu, K.[Kun],
Zhang, W.K.[Wen-Kai],
Weakly Supervised Semantic Segmentation in Aerial Imagery via
Cross-Image Semantic Mining,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Zhang, Y.F.[Yi-Fei],
Sidibé, D.[Désiré],
Morel, O.[Olivier],
Mériaudeau, F.[Fabrice],
Deep multimodal fusion for semantic image segmentation: A survey,
IVC(105), 2021, pp. 104042.
Elsevier DOI
2101
Survey, Semantic Segmentation. Image fusion, Multi-modal, Deep learning, Semantic segmentation
BibRef
Hu, S.[Sijie],
Bonardi, F.[Fabien],
Bouchafa, S.[Samia],
Sidibé, D.[Désiré],
Multi-modal unsupervised domain adaptation for semantic image
segmentation,
PR(137), 2023, pp. 109299.
Elsevier DOI
2302
Unsupervised domain adaptation, Multi-modal learning,
Self-supervised learning, Knowledge transfer, Semantic segmentation
BibRef
Wu, T.Y.[Tian-Yi],
Tang, S.[Sheng],
Zhang, R.[Rui],
Cao, J.[Juan],
Zhang, Y.D.[Yong-Dong],
CGNet: A Light-Weight Context Guided Network for Semantic
Segmentation,
IP(30), 2021, pp. 1169-1179.
IEEE DOI
2012
Semantics, Image segmentation, Context modeling,
Computer architecture, Computational modeling, Mobile handsets, context guided
BibRef
Yang, K.,
Hu, X.,
Stiefelhagen, R.,
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic
Segmentation in the Wild,
IP(30), 2021, pp. 1866-1881.
IEEE DOI
2101
Image segmentation, Semantics, Training, Cameras, Task analysis,
Benchmark testing, Context modeling, Scene understanding,
autonomous driving
BibRef
Kong, Y.Y.[Ying-Ying],
Liu, Y.J.[Yan-Juan],
Yan, B.Y.[Bi-Yuan],
Leung, H.[Henry],
Peng, X.Y.[Xiang-Yang],
A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation
Based on the Potential Energy Loss Function of Gibbs Distribution,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Zhang, P.P.[Ping-Ping],
Liu, W.[Wei],
Zeng, Y.[Yi],
Lei, Y.J.[Yin-Jie],
Lu, H.C.[Hu-Chuan],
Looking for the Detail and Context Devils: High-Resolution Salient
Object Detection,
IP(30), 2021, pp. 3204-3216.
IEEE DOI
2103
Feature extraction, Object detection, Head, Task analysis, Semantics,
Labeling, Data mining, Salient object detection,
boundary refinement
BibRef
Zeng, Y.[Yi],
Zhang, P.P.[Ping-Ping],
Lin, Z.[Zhe],
Zhang, J.M.[Jian-Ming],
Lu, H.C.[Hu-Chuan],
Towards High-Resolution Salient Object Detection,
ICCV19(7233-7242)
IEEE DOI
2004
image resolution, image segmentation, neural nets,
object detection, semantic networks, low resolutions,
Image resolution
BibRef
Liu, B.,
Jiao, J.,
Ye, Q.,
Harmonic Feature Activation for Few-Shot Semantic Segmentation,
IP(30), 2021, pp. 3142-3153.
IEEE DOI
2103
Semantics, Image segmentation, Feature extraction, Tensors,
Harmonic analysis, Fuses, Computational modeling,
bilinear model
BibRef
Michieli, U.[Umberto],
Zanuttigh, P.[Pietro],
Knowledge distillation for incremental learning in semantic
segmentation,
CVIU(205), 2021, pp. 103167.
Elsevier DOI
2103
BibRef
Earlier:
Incremental Learning Techniques for Semantic Segmentation,
TASKCV19(3205-3212)
IEEE DOI
2004
Incremental learning, Continual learning,
Semantic segmentation, Catastrophic forgetting, Knowledge distillation.
feature extraction, image classification,
image segmentation, learning (artificial intelligence),
Catastrophic Forgetting
BibRef
Toldo, M.[Marco],
Michieli, U.[Umberto],
Zanuttigh, P.[Pietro],
Learning With Style: Continual Semantic Segmentation Across Tasks and
Domains,
PAMI(46), No. 11, November 2024, pp. 7434-7450.
IEEE DOI
2410
Task analysis, Semantic segmentation, Training, Semantics,
Training data, Adaptation models, Object detection,
semantic segmentation
BibRef
Zhang, Y.[Yu],
Sun, X.[Xin],
Dong, J.Y.[Jun-Yu],
Chen, C.R.[Chang-Rui],
Lv, Q.X.[Qing-Xuan],
GPNet: Gated pyramid network for semantic segmentation,
PR(115), 2021, pp. 107940.
Elsevier DOI
2104
Deep learning, Semantic segmentation, Context embedding,
Gated mechanism, Attention
BibRef
Sun, P.[Peng],
Wu, J.X.[Jia-Xiang],
Li, S.Y.[Song-Yuan],
Lin, P.W.[Pei-Wen],
Huang, J.Z.[Jun-Zhou],
Li, X.[Xi],
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint
Decision and Feature Aggregation,
IJCV(129), No. 5, May 2021, pp. 1506-1525.
Springer DOI
2105
BibRef
Wang, D.L.[Dong-Li],
Li, N.J.[Nan-Jun],
Zhou, Y.[Yan],
Mu, J.Z.[Jin-Zhen],
Bilateral attention network for semantic segmentation,
IET-IPR(15), No. 8, 2021, pp. 1607-1616.
DOI Link
2106
BibRef
Yang, W.[Wei],
Zhang, J.L.[Jian-Lin],
Chen, Z.[Zhongbi],
Xu, Z.Y.[Zhi-Yong],
An efficient semantic segmentation method based on transfer learning
from object detection,
IET-IPR(15), No. 1, 2021, pp. 57-64.
DOI Link
2106
BibRef
Ahmed, I.A.L.[Ifham Abdul Latheef],
Jaward, M.H.[Mohamed Hisham],
Classifier aided training for semantic segmentation,
JVCIR(78), 2021, pp. 103177.
Elsevier DOI
2107
Scene understanding, Semantic segmentation, Deep learning
BibRef
Yuan, Y.H.[Yu-Hui],
Huang, L.[Lang],
Guo, J.Y.[Jian-Yuan],
Zhang, C.[Chao],
Chen, X.L.[Xi-Lin],
Wang, J.D.[Jing-Dong],
OCNet: Object Context for Semantic Segmentation,
IJCV(129), No. 8, August 2021, pp. 2375-2398.
Springer DOI
2108
BibRef
Huang, Z.L.[Zi-Long],
Wang, X.G.[Xing-Gang],
Wang, J.S.[Jia-Si],
Liu, W.Y.[Wen-Yu],
Wang, J.D.[Jing-Dong],
Weakly-Supervised Semantic Segmentation Network with Deep Seeded
Region Growing,
CVPR18(7014-7023)
IEEE DOI
1812
Image segmentation, Semantics, Training,
Visualization, Task analysis, Image color analysis
BibRef
He, J.Y.[Jun-Yan],
Liang, S.H.[Shi-Hua],
Wu, X.[Xiao],
Zhao, B.[Bo],
Zhang, L.[Lei],
MGSeg:
Multiple Granularity-Based Real-Time Semantic Segmentation Network,
IP(30), 2021, pp. 7200-7214.
IEEE DOI
2108
Semantics, Image segmentation, Real-time systems, Visualization,
Task analysis, Noise measurement, Feature extraction,
multiple granularity
BibRef
Grubiic, I.[Ivan],
Oric, M.[Marin],
egvic, S.[Sinia],
A baseline for semi-supervised learning of efficient semantic
segmentation models,
MVA21(1-5)
DOI Link
2109
Training, Perturbation methods, Semantics, Random access memory,
Object segmentation, Semisupervised learning, Streaming media
BibRef
Yu, C.Q.[Chang-Qian],
Gao, C.X.[Chang-Xin],
Wang, J.B.[Jing-Bo],
Yu, G.[Gang],
Shen, C.H.[Chun-Hua],
Sang, N.[Nong],
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time
Semantic Segmentation,
IJCV(129), No. 11, November 2021, pp. 3051-3068.
Springer DOI
2110
BibRef
Yu, C.Q.[Chang-Qian],
Wang, J.B.[Jing-Bo],
Peng, C.[Chao],
Gao, C.X.[Chang-Xin],
Yu, G.[Gang],
Sang, N.[Nong],
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic
Segmentation,
ECCV18(XIII: 334-349).
Springer DOI
1810
BibRef
Bao, Y.Q.[Yan-Qi],
Song, K.C.[Ke-Chen],
Wang, J.[Jie],
Huang, L.M.[Li-Ming],
Dong, H.G.[Hon-Gwen],
Yan, Y.H.[Yun-Hui],
Visible and thermal images fusion architecture for few-shot semantic
segmentation,
JVCIR(80), 2021, pp. 103306.
Elsevier DOI
2110
V-T semantic segmentation, Thermal images, Few-shot semantic segmentation
BibRef
Huang, Z.L.[Zi-Long],
Wei, Y.C.[Yun-Chao],
Wang, X.G.[Xing-Gang],
Liu, W.Y.[Wen-Yu],
Huang, T.S.[Thomas S.],
Shi, H.[Humphrey],
AlignSeg: Feature-Aligned Segmentation Networks,
PAMI(44), No. 1, January 2022, pp. 550-557.
IEEE DOI
2112
Semantics, Context modeling, Computer architecture,
Image segmentation, context alignment
BibRef
Ding, X.F.[Xiao-Feng],
Zeng, T.Y.[Tie-Yong],
Tang, J.[Jian],
Che, Z.P.[Zheng-Ping],
Peng, Y.X.[Ya-Xin],
SRRNet: A Semantic Representation Refinement Network for Image
Segmentation,
MultMed(25), 2023, pp. 5720-5732.
IEEE DOI
2311
BibRef
Ding, X.F.[Xiao-Feng],
Shen, C.M.[Chao-Min],
Che, Z.P.[Zheng-Ping],
Zeng, T.Y.[Tie-Yong],
Peng, Y.X.[Ya-Xin],
SCARF: A Semantic Constrained Attention Refinement Network for
Semantic Segmentation,
AVVision21(3002-3011)
IEEE DOI
2112
Adaptation models, Image segmentation,
Computational modeling, Semantics, Refining
BibRef
Yi, R.M.[Ru-Meng],
Huang, Y.P.[Ya-Ping],
Guan, Q.J.[Qing-Ji],
Pu, M.Y.[Meng-Yang],
Zhang, R.S.[Run-Sheng],
Learning From Pixel-Level Label Noise:
A New Perspective for Semi-Supervised Semantic Segmentation,
IP(31), 2022, pp. 623-635.
IEEE DOI
2112
Noise measurement, Annotations, Image segmentation, Semantics,
Task analysis, Training, Predictive models,
graph neural network
BibRef
Li, G.[Genling],
Li, L.[Liang],
Zhang, J.[Jiawan],
BiAttnNet: Bilateral Attention for Improving Real-Time Semantic
Segmentation,
SPLetters(29), 2022, pp. 46-50.
IEEE DOI
2202
Convolution, Semantics, Image segmentation, Tensors, Testing,
Spatial filters, Real-time systems, Image segmentation,
real-time semantic segmentation
BibRef
Li, G.[Genling],
Li, L.[Liang],
Zhang, J.[Jiawan],
Hierarchical Semantic Broadcasting Network for Real-Time Semantic
Segmentation,
SPLetters(29), 2022, pp. 309-313.
IEEE DOI
2202
Semantics, Feature extraction, Broadcasting, Convolution,
Real-time systems, Image resolution, Mathematical models,
real-time semantic segmentation
BibRef
Jia, D.[Dayu],
Cao, J.[Jiale],
Pan, J.[Jing],
Pang, Y.W.[Yan-Wei],
Multi-stream densely connected network for semantic segmentation,
IET-CV(16), No. 2, 2022, pp. 180-191.
DOI Link
2202
image processing, image segmentation
BibRef
Zhou, H.[Hao],
Qi, L.[Lu],
Huang, H.[Hai],
Yang, X.[Xu],
Wan, Z.L.[Zhao-Liang],
Wen, X.L.[Xiang-Long],
CANet: Co-attention network for RGB-D semantic segmentation,
PR(124), 2022, pp. 108468.
Elsevier DOI
2203
BibRef
Earlier: A1, A2, A5, A3, A4, Only:
RGB-D Co-Attention Network for Semantic Segmentation,
ACCV20(I:519-536).
Springer DOI
2103
RGB-D, Multi-modal fusion, Co-attention, Semantic segmentation
BibRef
Liu, Y.Z.[Ya-Zhou],
Chen, Y.L.[Yu-Liang],
Lasang, P.[Pongsak],
Sun, Q.S.[Qun-Sen],
Covariance Attention for Semantic Segmentation,
PAMI(44), No. 4, April 2022, pp. 1805-1818.
IEEE DOI
2203
Semantics, Covariance matrices, Feature extraction,
Image segmentation, Task analysis, Neural networks,
attention module
BibRef
Ru, L.X.[Li-Xiang],
Du, B.[Bo],
Zhan, Y.B.[Yi-Bing],
Wu, C.[Chen],
Weakly-Supervised Semantic Segmentation with Visual Words Learning and
Hybrid Pooling,
IJCV(130), No. 1, January 2022, pp. 1127-1144.
Springer DOI
2204
BibRef
Ru, L.X.[Li-Xiang],
Zhan, Y.B.[Yi-Bing],
Yu, B.S.[Bao-Sheng],
Du, B.[Bo],
Learning Affinity from Attention: End-to-End Weakly-Supervised
Semantic Segmentation with Transformers,
CVPR22(16825-16834)
IEEE DOI
2210
Training, Image segmentation, Semantics, Computer architecture,
Transformers,
grouping and shape analysis
BibRef
Zhang, X.[Xiang],
Zhao, W.Q.[Wan-Qing],
Zhang, W.[Wei],
Peng, J.Y.[Jin-Ye],
Fan, J.P.[Jian-Ping],
Guided Filter Network for Semantic Image Segmentation,
IP(31), 2022, pp. 2695-2709.
IEEE DOI
2204
Image segmentation, Semantics, Training, Feature extraction,
Labeling, Manuals, Knowledge engineering,
deep networks
BibRef
He, X.J.[Xing-Jian],
Liu, J.[Jing],
Wang, W.N.[Wei-Ning],
Lu, H.Q.[Han-Qing],
An Efficient Sampling-Based Attention Network for Semantic
Segmentation,
IP(31), No. 2022, pp. 2850-2863.
IEEE DOI
2204
Stochastic processes, Sampling methods, Semantics,
Image segmentation, Computational complexity,
deterministic sampling-based attention
BibRef
Pan, J.W.[Jun-Wen],
Zhu, P.F.[Peng-Fei],
Zhang, K.H.[Kai-Hua],
Cao, B.[Bing],
Wang, Y.[Yu],
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
Hu, Q.H.[Qing-Hua],
Learning Self-supervised Low-Rank Network for Single-Stage Weakly and
Semi-Supervised Semantic Segmentation,
IJCV(130), No. 5, May 2022, pp. 1181-1195.
Springer DOI
2205
BibRef
Li, J.Y.[Jiang-Yun],
Zha, S.[Sen],
Chen, C.[Chen],
Ding, M.[Meng],
Zhang, T.X.[Tian-Xiang],
Yu, H.[Hong],
Attention Guided Global Enhancement and Local Refinement Network for
Semantic Segmentation,
IP(31), 2022, pp. 3211-3223.
IEEE DOI
2205
Semantics, Decoding, Image segmentation, Interpolation, Convolution,
Aggregates, Context modeling, Semantic segmentation,
context fusion
BibRef
Cha, S.[Sungguk],
Wang, Y.[Yooseung],
Zero-shot semantic segmentation via spatial and multi-scale aware
visual class embedding,
PRL(158), 2022, pp. 87-93.
Elsevier DOI
2205
Semantic segmentation, Zero-shot learning,
Convolutional neural networks, Zero-shot semantic segmentation
BibRef
Vu, M.H.[Minh H.],
Norman, G.[Gabriella],
Nyholm, T.[Tufve],
Löfstedt, T.[Tommy],
A Data-Adaptive Loss Function for Incomplete Data and Incremental
Learning in Semantic Image Segmentation,
MedImg(41), No. 6, June 2022, pp. 1320-1330.
IEEE DOI
2206
Data models, Training, Predictive models, Computational modeling,
Adaptation models, Task analysis, Image segmentation,
and semantic image segmentation
BibRef
Zhuang, B.[Bohan],
Shen, C.H.[Chun-Hua],
Tan, M.K.[Ming-Kui],
Chen, P.[Peng],
Liu, L.Q.[Ling-Qiao],
Reid, I.D.[Ian D.],
Structured Binary Neural Networks for Image Recognition,
IJCV(130), No. 9, September 2022, pp. 2081-2102.
Springer DOI
2208
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Structured Binary Neural Networks for Accurate Image Classification and
Semantic Segmentation,
CVPR19(413-422).
IEEE DOI
2002
BibRef
Xie, B.Q.[Bang-Quan],
Yang, Z.M.[Zong-Ming],
Yang, L.[Liang],
Luo, R.[Ruifa],
Wei, A.[Ailin],
Weng, X.X.[Xiao-Xiong],
Li, B.[Bing],
Multi-Scale Fusion With Matching Attention Model: A Novel Decoding
Network Cooperated With NAS for Real-Time Semantic Segmentation,
ITS(23), No. 8, August 2022, pp. 12622-12632.
IEEE DOI
2208
Feature extraction, Semantics, Real-time systems,
Computer architecture, Image segmentation, Encoding, Decoding,
autonomous driving
BibRef
Li, J.H.[Jie-Hao],
Dai, Y.P.[Ying-Peng],
Su, X.H.[Xiao-Hang],
Wu, W.B.[Wei-Bin],
Efficient Dual-Branch Bottleneck Networks of Semantic Segmentation
Based on CCD Camera,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Choe, J.[Junsuk],
Han, D.Y.[Dong-Yoon],
Yun, S.[Sangdoo],
Ha, J.W.[Jung-Woo],
Oh, S.J.[Seong Joon],
Shim, H.J.[Hyun-Jung],
Region-based dropout with attention prior for weakly supervised
object localization,
PR(116), 2021, pp. 107949.
Elsevier DOI
2106
Deep learning, Object localization, Weakly supervised learning,
Region-based dropout, Attention prior
BibRef
Choe, J.[Junsuk],
Lee, S.[Seungho],
Shim, H.J.[Hyun-Jung],
Attention-Based Dropout Layer for Weakly Supervised Single Object
Localization and Semantic Segmentation,
PAMI(43), No. 12, December 2021, pp. 4256-4271.
IEEE DOI
2112
BibRef
Earlier: A1, A3, Only:
Attention-Based Dropout Layer for Weakly Supervised Object Localization,
CVPR19(2214-2223).
IEEE DOI
2002
Semantics, Training data, Image segmentation, Feature extraction,
Computational modeling, Convolutional codes, Location awareness,
semantic segmentation
BibRef
Lee, J.[Jungbeom],
Oh, S.J.[Seong Joon],
Yun, S.[Sangdoo],
Choe, J.[Junsuk],
Kim, E.[Eunji],
Yoon, S.[Sungroh],
Weakly Supervised Semantic Segmentation using Out-of-Distribution
Data,
CVPR22(16876-16885)
IEEE DOI
2210
Rails, Training, Location awareness, Visualization,
Image segmentation, Image analysis, Shape,
grouping and shape analysis
BibRef
Lee, J.[Jungbeom],
Kim, E.[Eunji],
Lee, S.M.[Sung-Min],
Lee, J.[Jangho],
Yoon, S.[Sungroh],
FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using
Stochastic Inference,
CVPR19(5262-5271).
IEEE DOI
2002
BibRef
Ding, H.H.[Heng-Hui],
Zhang, H.[Hui],
Jiang, X.D.[Xu-Dong],
Self-regularized prototypical network for few-shot semantic
segmentation,
PR(133), 2023, pp. 109018.
Elsevier DOI
2210
Few-shot segmentation, Prototype, Prototypical network,
Self-regularized, Non-parametric distance fidelity, CNN
BibRef
Lei, X.C.[Xiao-Chun],
Lu, L.J.[Lin-Jun],
Jiang, Z.T.[Ze-Tao],
Gong, Z.T.[Zhao-Ting],
Lu, C.[Chang],
Liang, J.M.[Jia-Ming],
Xie, J.L.[Jun-Lin],
STDC-MA network for semantic segmentation,
IET-IPR(16), No. 14, 2022, pp. 3758-3767.
DOI Link
2212
BibRef
Padalkar, G.R.[Ganesh R.],
Khambete, M.B.[Madhuri B.],
Fusion-Based Semantic Segmentation Using Deep Learning Architecture in
Case of Very Small Training Dataset,
IJIG(22), No. 5 2022, pp. 2250043.
DOI Link
2212
BibRef
Islam, M.A.[Md Amirul],
Kowal, M.[Matthew],
Derpanis, K.G.[Konstantinos G.],
Bruce, N.D.B.[Neil D. B.],
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and
Adversarial Robustness,
IJCV(131), No. 3, March 2023, pp. 701-716.
Springer DOI
2302
BibRef
Karim, R.[Rezaul],
Islam, M.A.[M. Amirul],
Bruce, N.D.B.[Neil D. B.],
Distributed Iterative Gating Networks for Semantic Segmentation,
WACV20(2833-2842)
IEEE DOI
2006
BibRef
Earlier:
Recurrent Iterative Gating Networks for Semantic Segmentation,
WACV19(1070-1079)
IEEE DOI
1904
Semantics, Logic gates, Feeds, Modulation, Spatial resolution,
Labeling, Signal resolution.
image segmentation, iterative methods,
learning (artificial intelligence), neural net architecture.
BibRef
Singha, T.[Tanmay],
Pham, D.S.[Duc-Son],
Krishna, A.[Aneesh],
A real-time semantic segmentation model using iteratively shared
features in multiple sub-encoders,
PR(140), 2023, pp. 109557.
Elsevier DOI
2305
Semantic segmentation, Deep convolution neural networks, Multi-encoder,
Decoder, Feature scaling, Feature aggregation, Mobile devices
BibRef
Singha, T.[Tanmay],
Pham, D.S.[Duc-Son],
Krishna, A.[Aneesh],
FANet: Feature Aggregation Network for Semantic Segmentation,
DICTA20(1-8)
IEEE DOI
2201
Performance evaluation, Image segmentation, Service robots,
Computational modeling, Semantics, Real-time systems,
MobileNet
BibRef
Singha, T.[Tanmay],
Bergemann, M.[Moritz],
Pham, D.S.[Duc-Son],
Krishna, A.[Aneesh],
SCMNet: Shared Context Mining Network for Real-time Semantic
Segmentation,
DICTA21(1-8)
IEEE DOI
2201
Location awareness, Image segmentation, Image resolution,
Annotations, Computational modeling, Semantics, Predictive models,
DCNNs
BibRef
Li, Q.P.[Qiu-Peng],
Kong, Y.Y.[Ying-Ying],
An Improved SAR Image Semantic Segmentation Deeplabv3+ Network Based
on the Feature Post-Processing Module,
RS(15), No. 8, 2023, pp. 2153.
DOI Link
2305
BibRef
Bi, Q.[Qi],
You, S.D.[Shao-Di],
Gevers, T.[Theo],
Interactive Learning of Intrinsic and Extrinsic Properties for
All-Day Semantic Segmentation,
IP(32), 2023, pp. 3821-3835.
IEEE DOI
2307
Semantics, Semantic segmentation, Lighting, Training,
Image representation, Benchmark testing, Task analysis,
intrinsic and extrinsic properties
BibRef
Cheng, X.[Xu],
Li, H.Y.[Hao-Yuan],
Deng, S.Y.[Shu-Ya],
Peng, Y.H.[Yong-Hong],
POEM: A prototype cross and emphasis network for few-shot semantic
segmentation,
CVIU(234), 2023, pp. 103746.
Elsevier DOI
2307
Few-shot semantic segmentation, Semantic segmentation,
Customized prototypes, Convolutional neural network, Few-shot learning
BibRef
Gao, G.W.[Guang-Wei],
Xu, G.[Guoan],
Li, J.C.[Jun-Cheng],
Yu, Y.[Yi],
Lu, H.M.[Hui-Min],
Yang, J.[Jian],
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic
Segmentation,
MultMed(25), 2023, pp. 3273-3283.
IEEE DOI
2309
BibRef
Xu, J.S.[Jing-Shan],
Zhou, C.W.[Chuan-Wei],
Cui, Z.[Zhen],
Xu, C.Y.[Chun-Yan],
Huang, Y.[Yuge],
Shen, P.C.[Peng-Cheng],
Li, S.X.[Shao-Xin],
Yang, J.[Jian],
Scribble-Supervised Semantic Segmentation Inference,
ICCV21(15334-15343)
IEEE DOI
2203
Image segmentation, Graphical models, Semantics,
Superluminescent diodes, Scene analysis and understanding,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Wang, Y.F.[Yue-Fei],
Yu, X.[Xi],
Guo, X.Y.[Xiao-Yan],
Wang, X.L.[Xi-Lei],
Wei, Y.H.[Yuan-Hong],
Zeng, S.J.[Shi-Jie],
A Dual-Decoding branch U-shaped semantic segmentation network
combining Transformer attention with Decoder: DBUNet,
JVCIR(95), 2023, pp. 103856.
Elsevier DOI
2309
Semantic Segmentation, U-Shaped Network, Transformer ViT, Medical Image
BibRef
Fu, S.[Siming],
Wang, H.[Hualiang],
Hu, H.J.[Hao-Ji],
He, X.X.[Xiao-Xuan],
Long, Y.[Yongwen],
Bai, J.H.[Jian-Hong],
Ou, Y.T.[Yang-Tao],
Huang, Y.[Yuanjia],
Zhou, M.Q.[Meng-Qiu],
Class semantic enhancement network for semantic segmentation,
JVCIR(96), 2023, pp. 103924.
Elsevier DOI
2310
Semantic segmentation, Attention, Graph module
BibRef
Yang, G.L.[Guang-Lei],
Fini, E.[Enrico],
Xu, D.[Dan],
Rota, P.[Paolo],
Ding, M.L.[Ming-Li],
Tang, H.[Hao],
Alameda-Pineda, X.[Xavier],
Ricci, E.[Elisa],
Continual Attentive Fusion for Incremental Learning in Semantic
Segmentation,
MultMed(25), 2023, pp. 3841-3854.
IEEE DOI
2310
BibRef
Xu, M.D.[Meng-De],
Zhang, Z.[Zheng],
Wei, F.Y.[Fang-Yun],
Hu, H.[Han],
Bai, X.[Xiang],
SAN: Side Adapter Network for Open-Vocabulary Semantic Segmentation,
PAMI(45), No. 12, December 2023, pp. 15546-15561.
IEEE DOI
2311
BibRef
Earlier:
Side Adapter Network for Open-Vocabulary Semantic Segmentation,
CVPR23(2945-2954)
IEEE DOI
2309
BibRef
Ma, Y.Z.[Yi-Zhe],
Lin, F.[Fangjian],
Wu, S.[Sitong],
Tian, S.W.[Sheng-Wei],
Yu, L.[Long],
PRSeg:
A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation,
CirSysVideo(33), No. 11, November 2023, pp. 6860-6871.
IEEE DOI
2311
BibRef
Yin, X.[Xu],
Min, D.B.[Dong-Bo],
Huo, Y.[Yuchi],
Yoon, S.E.[Sung-Eui],
Contour-Aware Equipotential Learning for Semantic Segmentation,
MultMed(25), 2023, pp. 6146-6156.
IEEE DOI
2311
BibRef
Zhou, X.C.[Xi-Chuan],
Ding, R.[Rui],
Wang, Y.X.[Yu-Xiao],
Wei, W.J.[Wen-Jia],
Liu, H.J.[Hai-Jun],
Cellular Binary Neural Network for Accurate Image Classification and
Semantic Segmentation,
MultMed(25), 2023, pp. 8064-8075.
IEEE DOI
2312
BibRef
Liu, Z.[Zhi],
Zhang, Y.[Yi],
Guo, X.J.[Xiao-Jie],
Boosting semantic segmentation via feature enhancement,
JVCIR(92), 2023, pp. 103796.
Elsevier DOI
2303
Semantic segmentation, Feature enhancement, Deep learning
BibRef
Liu, J.B.[Jian-Bo],
He, J.J.[Jun-Jun],
Zheng, Y.J.[Yuan-Jie],
Yi, S.[Shuai],
Wang, X.G.[Xiao-Gang],
Li, H.S.[Hong-Sheng],
A Holistically-Guided Decoder for Deep Representation Learning With
Applications to Semantic Segmentation and Object Detection,
PAMI(45), No. 10, October 2023, pp. 11390-11406.
IEEE DOI
2310
BibRef
Zhang, K.P.[Kai-Peng],
Sato, Y.[Yoichi],
Semantic Image Segmentation by Dynamic Discriminative Prototypes,
MultMed(26), 2024, pp. 737-749.
IEEE DOI
2402
Prototypes, Testing, Training, Semantic segmentation, Feature extraction,
Semantics, Task analysis, Deep learning, semantic segmentation
BibRef
Luo, X.L.[Xiao-Liu],
Tian, Z.T.[Zhuo-Tao],
Zhang, T.P.[Tai-Ping],
Yu, B.[Bei],
Tang, Y.Y.[Yuan Yan],
Jia, J.Y.[Jia-Ya],
PFENet++: Boosting Few-Shot Semantic Segmentation With the
Noise-Filtered Context-Aware Prior Mask,
PAMI(46), No. 2, February 2024, pp. 1273-1289.
IEEE DOI
2401
BibRef
Lai, X.[Xin],
Tian, Z.T.[Zhuo-Tao],
Jiang, L.[Li],
Liu, S.[Shu],
Zhao, H.S.[Heng-Shuang],
Wang, L.W.[Li-Wei],
Jia, J.Y.[Jia-Ya],
Semi-supervised Semantic Segmentation with Directional Context-aware
Consistency,
CVPR21(1205-1214)
IEEE DOI
2111
Image segmentation, Annotations, Semantics,
Training data, Data models, Pattern recognition
BibRef
Shen, T.C.[Tian-Cheng],
Zhang, Y.C.[Yue-Chen],
Qi, L.[Lu],
Kuen, J.[Jason],
Xie, X.Y.[Xing-Yu],
Wu, J.L.[Jian-Long],
Lin, Z.[Zhe],
Jia, J.Y.[Jia-Ya],
High Quality Segmentation for Ultra High-Resolution Images,
CVPR22(1300-1309)
IEEE DOI
2210
Training, Image segmentation, Visualization,
Computational modeling, Aggregates, Computer vision for social good
BibRef
Lee, J.[Jungbeom],
Kim, E.[Eunji],
Mok, J.[Jisoo],
Yoon, S.[Sungroh],
Anti-Adversarially Manipulated Attributions for Weakly Supervised
Semantic Segmentation and Object Localization,
PAMI(46), No. 3, March 2024, pp. 1618-1634.
IEEE DOI
2402
BibRef
Earlier: A1, A2, A4, Only:
Anti-Adversarially Manipulated Attributions for Weakly and
Semi-Supervised Semantic Segmentation,
CVPR21(4070-4078)
IEEE DOI
2111
Semantics, Location awareness, Image segmentation, Annotations, Training,
Perturbation methods, Artificial neural networks, object localization.
Object recognition
BibRef
Ye, M.T.[Meng-Ting],
Chen, Z.[Zhenxue],
Guo, Y.X.[Yi-Xin],
Yu, K.[Kaili],
Liu, L.C.[Long-Cheng],
Wu, Q.M.J.[Q.M. Jonathan],
BNDCNet: Bilateral nonlocal decoupled convergence network for
semantic segmentation,
JVCIR(98), 2024, pp. 104028.
Elsevier DOI Code:
WWW Link.
2402
Semantic segmentation, Scene parsing,
Contextual information aggregation, Non-local modules
BibRef
Ren, F.L.[Feng-Lei],
Zhou, H.B.[Hai-Bo],
Yang, L.[Lu],
Bai, Y.W.[Yi-Wen],
Xu, W.X.[Wen-Xue],
STDBNet: Shared Trunk and Dual-Branch Network for Real-Time Semantic
Segmentation,
SPLetters(31), 2024, pp. 770-774.
IEEE DOI
2404
Semantics, Semantic segmentation, Real-time systems,
Feature extraction, Convolution, Signal processing algorithms,
semantic segmentation
BibRef
Cong, R.[Runmin],
Xiong, H.[Hang],
Chen, J.P.[Jin-Peng],
Zhang, W.[Wei],
Huang, Q.M.[Qing-Ming],
Zhao, Y.[Yao],
Query-Guided Prototype Evolution Network for Few-Shot Segmentation,
MultMed(26), 2024, pp. 6501-6512.
IEEE DOI
2404
Prototypes, Training, Semantic segmentation, Task analysis,
Information science, Computer architecture, Annotations,
prototype generation
BibRef
Liu, J.[Jie],
Yin, W.Z.[Wen-Zhe],
Wang, H.C.[Hao-Chen],
Chen, Y.L.[Yun-Lu],
Sonke, J.J.[Jan-Jakob],
Gavves, E.[Efstratios],
Dynamic Prototype Adaptation with Distillation for Few-shot Point
Cloud Segmentation,
3DV24(810-819)
IEEE DOI Code:
WWW Link.
2408
Point cloud compression, Codes, Aggregates, Prototypes,
Benchmark testing, Feature extraction, Point cloud segmetnation,
dynamic adaptation
BibRef
Liu, J.[Jie],
Bao, Y.Q.[Yan-Qi],
Xie, G.S.[Guo-Sen],
Xiong, H.[Huan],
Sonke, J.J.[Jan-Jakob],
Gavves, E.[Efstratios],
Dynamic Prototype Convolution Network for Few-Shot Semantic
Segmentation,
CVPR22(11543-11552)
IEEE DOI
2210
Training, Image segmentation, Convolution, Semantics, Prototypes,
Information filters, Segmentation, grouping and shape analysis,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Xie, G.S.[Guo-Sen],
Xiong, H.[Huan],
Liu, J.[Jie],
Yao, Y.Z.[Ya-Zhou],
Shao, L.[Ling],
Few-Shot Semantic Segmentation with Cyclic Memory Network,
ICCV21(7273-7282)
IEEE DOI
2203
Semantics, Prototypes, Object segmentation, Task analysis,
Spatial resolution, Multiresolution analysis, Segmentation,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Xie, G.S.[Guo-Sen],
Liu, J.[Jie],
Xiong, H.[Huan],
Shao, L.[Ling],
Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation,
CVPR21(5471-5480)
IEEE DOI
2111
Frequency selective surfaces, Image segmentation,
Message passing, Semantics, Collaboration, Prototypes, Graph neural networks
BibRef
Görmez, A.[Alperen],
Koyuncu, E.[Erdem],
Class Based Thresholding in Early Exit Semantic Segmentation Networks,
SPLetters(31), 2024, pp. 1184-1188.
IEEE DOI
2405
Computational modeling, Semantic segmentation, Task analysis,
Computational efficiency, Training, Costs, Predictive models, segmentation
BibRef
Jiang, R.[Rui],
Chen, R.[Runa],
Zhang, L.[Li],
Wang, X.M.[Xiao-Ming],
Xu, Y.[Youyun],
AM-MulFSNet: A fast semantic segmentation network combining attention
mechanism and multi-branch,
IET-IPR(18), No. 7, 2024, pp. 1733-1744.
DOI Link Code:
WWW Link.
2405
convolutional neural nets, feature extraction,
image processing, image segmentation
BibRef
Shi, M.[Min],
Lin, S.[Shaowen],
Yi, Q.M.[Qing-Ming],
Weng, J.[Jian],
Luo, A.[Aiwen],
Zhou, Y.C.[Yi-Cong],
Lightweight Context-Aware Network Using Partial-Channel
Transformation for Real-Time Semantic Segmentation,
ITS(25), No. 7, July 2024, pp. 7401-7416.
IEEE DOI Code:
WWW Link.
2407
Semantic segmentation, Convolution, Computational modeling,
Feature extraction, Real-time systems, Stacking, Semantics, spatial attention
BibRef
Jin, Z.[Zhenyi],
Dou, F.[Furong],
Feng, Z.L.[Zi-Liang],
Zhang, C.F.[Cheng-Fang],
BSNet: A bilateral real-time semantic segmentation network based on
multi-scale receptive fields,
JVCIR(102), 2024, pp. 104188.
Elsevier DOI
2407
Road scenes, Real-time semantic segmentation,
Multi-scale receptive fields Bilateral network, Short-term dense concatenate
BibRef
He, M.F.[Meng-Fei],
Yang, Z.Y.[Zhi-You],
Zhang, G.B.[Guang-Ben],
Long, Y.[Yan],
Song, H.B.[Huai-Bo],
IIMT-net: Poly-1 weights balanced multi-task network for semantic
segmentation and depth estimation using interactive information,
IVC(148), 2024, pp. 105109.
Elsevier DOI
2407
Multi-task learning, Scene understanding,
Semantic segmentation, Depth estimation, Vision transformer
BibRef
Qiu, S.[Shoumeng],
Chen, J.[Jie],
Zhang, H.Q.[Hai-Qiang],
Wan, R.[Ru],
Xue, X.Y.[Xiang-Yang],
Pu, J.[Jian],
Guided contrastive boundary learning for semantic segmentation,
PR(155), 2024, pp. 110723.
Elsevier DOI Code:
WWW Link.
2408
Semantic segmentation, Contrastive learning, Boundary optimization
BibRef
Wang, B.Y.[Bao-Yu],
Shen, A.[Aihong],
Dong, X.[Xu],
Cao, P.P.[Ping-Ping],
CF-Net: Cross fusion network for semantic segmentation,
IET-IPR(18), No. 12, 2024, pp. 3587-3599.
DOI Link
2411
image segmentation
BibRef
Sung, C.K.[Chang-Ki],
Kim, W.[Wanhee],
An, J.[Jungho],
Lee, W.J.[Woo-Ju],
Lim, H.[Hyungtae],
Myung, H.[Hyun],
Contextrast: Contextual Contrastive Learning for Semantic
Segmentation,
CVPR24(3732-3742)
IEEE DOI
2410
Semantic segmentation, Contrastive learning,
Computational efficiency, Contrastive learning
BibRef
Ye, D.Q.[Ding-Qiang],
Fan, C.[Chao],
Ma, J.Z.[Jing-Zhe],
Liu, X.M.[Xiao-Ming],
Yu, S.Q.[Shi-Qi],
BigGait: Learning Gait Representation You Want by Large Vision Models,
CVPR24(200-210)
IEEE DOI Code:
WWW Link.
2410
Industries, Costs, Annotations, Source coding, Supervised learning,
Large Vision Models, Gait Recognition, Person Re-Identification
BibRef
Rottmann, M.[Matthias],
Reese, M.[Marco],
Automated Detection of Label Errors in Semantic Segmentation Datasets
via Deep Learning and Uncertainty Quantification,
WACV23(3213-3222)
IEEE DOI
2302
Deep learning, Degradation, Uncertainty, Annotations,
Semantic segmentation, Neural networks, segmentation)
BibRef
Lin, Z.[Zihan],
Wang, Z.[Zilei],
Zhang, Y.X.[Yi-Xin],
Preparing the Future for Continual Semantic Segmentation,
ICCV23(11876-11886)
IEEE DOI
2401
BibRef
Liu, Y.[Yuyuan],
Ding, C.[Choubo],
Tian, Y.[Yu],
Pang, G.S.[Guan-Song],
Belagiannis, V.[Vasileios],
Reid, I.D.[Ian D.],
Carneiro, G.[Gustavo],
Residual Pattern Learning for Pixel-wise Out-of-Distribution
Detection in Semantic Segmentation,
ICCV23(1151-1161)
IEEE DOI
2401
BibRef
Wang, Z.Y.[Zhi-Yan],
Guo, X.[Xin],
Wang, S.[Song],
Zheng, P.X.[Pei-Xiao],
Qi, L.[Lin],
A Feature Refinement Module for Light-Weight Semantic Segmentation
Network,
ICIP23(2035-2039)
IEEE DOI
2312
BibRef
Kaiser, T.[Timo],
Reinders, C.[Christoph],
Rosenhahn, B.[Bodo],
Compensation Learning in Semantic Segmentation,
VDU23(3267-3278)
IEEE DOI
2309
BibRef
Xu, J.[Jiacong],
Xiong, Z.X.[Zi-Xiang],
Bhattacharyya, S.P.[Shankar P.],
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID
Controllers,
CVPR23(19529-19539)
IEEE DOI
2309
BibRef
Maiti, A.[Abhisek],
Elberink, S.O.[Sander Oude],
Vosselman, G.[George],
TransFusion: Multi-modal Fusion Network for Semantic Segmentation,
PCV23(6537-6547)
IEEE DOI
2309
BibRef
Islam, A.[Ashraful],
Lundell, B.[Ben],
Sawhney, H.[Harpreet],
Sinha, S.N.[Sudipta N.],
Morales, P.[Peter],
Radke, R.J.[Richard J.],
Self-supervised Learning with Local Contrastive Loss for Detection
and Semantic Segmentation,
WACV23(5613-5622)
IEEE DOI
2302
Training, Semantic segmentation, Self-supervised learning,
Object detection, Task analysis, segmentation
BibRef
Zhu, G.[Guilin],
Wang, R.[Runmin],
Han, C.[Chang],
Liu, Y.Y.[Ying-Ying],
Ding, Y.J.[Ya-Jun],
Liu, M.H.[Ming-Hao],
Liu, L.[Li],
Sang, N.[Nong],
RFNet: A Refinement Network for Semantic Segmentation,
ICPR22(670-676)
IEEE DOI
2212
Training, Image color analysis, Semantic segmentation,
Image edge detection, Memory management, Predictive models
BibRef
Jin, Z.C.[Zhen-Chao],
Yu, D.D.[Dong-Dong],
Song, L.C.[Lu-Chuan],
Yuan, Z.H.[Ze-Huan],
Yu, L.Q.[Le-Quan],
You Should Look at All Objects,
ECCV22(IX:332-349).
Springer DOI
2211
WWW Link. Future Pyramid Network. Training for varied scale.
BibRef
Wang, L.J.[Lin-Jie],
Zhou, Q.[Quan],
Jiang, C.F.[Chen-Feng],
Wu, X.[Xiaofu],
Latecki, L.J.[Longin Jan],
DRBANET: A Lightweight Dual-Resolution Network for Semantic
Segmentation with Boundary Auxiliary,
ICIP22(531-535)
IEEE DOI
2211
Head, Semantics, Parallel architectures, Lightweight network,
Semantic segmentation, Boundary supervision, Dual-resolution network
BibRef
Jiang, T.J.[Tian-Jiao],
Jin, Y.[Yi],
Liang, T.F.[Teng-Fei],
Wang, X.[Xu],
Li, Y.D.[Yi-Dong],
Boundary Corrected Multi-Scale Fusion Network for Real-Time Semantic
Segmentation,
ICIP22(1886-1890)
IEEE DOI
2211
Image resolution, Computational modeling, Roads, Semantics,
Feature extraction, Real-time systems, Semantic segmentation, Boundary loss
BibRef
Shi, H.M.[Hui-Min],
Zhou, Q.[Quan],
Ni, Y.H.[Ying-Hao],
Wu, X.[Xiaofu],
Latecki, L.J.[Longin Jan],
DPNET: Dual-Path Network for Efficient Object Detection with
Lightweight Self-Attention,
ICIP22(771-775)
IEEE DOI
2211
Performance evaluation, Costs, Image edge detection,
Neural networks, Object detection, Computational efficiency,
convolution neural network
BibRef
Kim, J.Y.[Jih-Yun],
Jeong, S.[Somi],
Sohn, K.H.[Kwang-Hoon],
PASTS: Toward Effective Distilling Transformer for Panoramic Semantic
Segmentation,
ICIP22(2881-2885)
IEEE DOI
2211
Semantics, Force, Imaging, Transformers, Feature extraction,
Distortion, Entropy, Semantic segmentation, panoramic image,
knowledge distillation
BibRef
Kouris, A.[Alexandros],
Venieris, S.I.[Stylianos I.],
Laskaridis, S.[Stefanos],
Lane, N.[Nicholas],
Multi-Exit Semantic Segmentation Networks,
ECCV22(XXI:330-349).
Springer DOI
2211
BibRef
Yang, L.[Lihe],
Zhuo, W.[Wei],
Qi, L.[Lei],
Shi, Y.H.[Ying-Huan],
Gao, Y.[Yang],
ST++: Make Self-trainingWork Better for Semi-supervised Semantic
Segmentation,
CVPR22(4258-4267)
IEEE DOI
2210
Training, Image segmentation, Semantics, Pipelines,
Predictive models, Stability analysis,
Self- semi- meta- unsupervised learning
BibRef
Chen, B.H.[Bing-Hui],
Li, P.Y.[Peng-Yu],
Chen, X.[Xiang],
Wang, B.[Biao],
Zhang, L.[Lei],
Hua, X.S.[Xian-Sheng],
Dense Learning based Semi-Supervised Object Detection,
CVPR22(4805-4814)
IEEE DOI
2210
Training, Bridges, Uncertainty, Codes, Filtering, Detectors,
Recognition: detection, categorization, retrieval,
Self- semi- meta- unsupervised learning
BibRef
Cermelli, F.[Fabio],
Fontanel, D.[Dario],
Tavera, A.[Antonio],
Ciccone, M.[Marco],
Caputo, B.[Barbara],
Incremental Learning in Semantic Segmentation from Image Labels,
CVPR22(4361-4371)
IEEE DOI
2210
Learning systems, Location awareness, Image segmentation,
Protocols, Annotations, Semantics, Segmentation,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Yang, F.[Fan],
Wu, K.[Kai],
Zhang, S.Y.[Shu-Yi],
Jiang, G.[Guannan],
Liu, Y.[Yong],
Zheng, F.[Feng],
Zhang, W.[Wei],
Wang, C.J.[Cheng-Jie],
Zeng, L.[Long],
Class-Aware Contrastive Semi-Supervised Learning,
CVPR22(14401-14410)
IEEE DOI
2210
Training, Semisupervised learning, Robustness, Data models,
Noise measurement, Self- semi- meta- unsupervised learning
BibRef
Melas-Kyriazi, L.[Luke],
Rupprecht, C.[Christian],
Laina, I.[Iro],
Vedaldi, A.[Andrea],
Deep Spectral Methods: A Surprisingly Strong Baseline for
Unsupervised Semantic Segmentation and Localization,
CVPR22(8354-8365)
IEEE DOI
2210
Location awareness, Deep learning, Image segmentation, Semantics,
Transformers, Graph theory, Self- semi- meta- Segmentation,
grouping and shape analysis
BibRef
Kim, J.[Jin],
Lee, J.Y.[Ji-Young],
Park, J.[Jungin],
Min, D.B.[Dong-Bo],
Sohn, K.H.[Kwang-Hoon],
Pin the Memory: Learning to Generalize Semantic Segmentation,
CVPR22(4340-4350)
IEEE DOI
2210
Deep learning, Image analysis, Shape, Semantics, Neural networks,
Benchmark testing, Segmentation, grouping and shape analysis,
Self- semi- meta- unsupervised learning
BibRef
Shin, G.G.[Gyun-Gin],
Xie, W.[Weidi],
Albanie, S.[Samuel],
All you need are a few pixels: semantic segmentation with PixelPick,
ILDAV21(1687-1697)
IEEE DOI
2112
Training, Deep learning, Costs, Sensitivity, Annotations, Semantics,
Pipelines
BibRef
Liu, M.Y.[Ming-Yuan],
Schonfeld, D.[Dan],
Tang, W.[Wei],
Exploit Visual Dependency Relations for Semantic Segmentation,
CVPR21(9721-9730)
IEEE DOI
2111
Training, Deep learning, Visualization, Semantics,
Network architecture, Cognition
BibRef
Adilova, L.[Linara],
Schulz, E.[Elena],
Akila, M.[Maram],
Houben, S.[Sebastian],
Schneider, J.D.[Jan David],
Hüger, F.[Fabian],
Wirtz, T.[Tim],
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable
Semantic Segmentation,
SAIAD21(85-92)
IEEE DOI
2109
Deep learning, Image segmentation, Semantics, Pipelines,
Probabilistic logic, Distortion
BibRef
Zhou, Y.N.[Yan-Ning],
Xu, H.[Hang],
Zhang, W.[Wei],
Gao, B.[Bin],
Heng, P.A.[Pheng-Ann],
C3-SemiSeg: Contrastive Semi-supervised Segmentation via Cross-set
Learning and Dynamic Class-balancing,
ICCV21(7016-7025)
IEEE DOI
2203
Training, Image segmentation, Perturbation methods, Semantics,
Pipelines, Semisupervised learning, Segmentation,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Hu, H.Z.[Han-Zhe],
Cui, J.S.[Jin-Shi],
Wang, L.W.[Li-Wei],
Region-aware Contrastive Learning for Semantic Segmentation,
ICCV21(16271-16281)
IEEE DOI
2203
Training, Learning systems, Image segmentation, Costs, Correlation,
Semantics, Memory management, Scene analysis and understanding,
grouping and shape
BibRef
Zhao, X.Y.[Xiang-Yun],
Vemulapalli, R.[Raviteja],
Mansfield, P.A.[Philip Andrew],
Gong, B.Q.[Bo-Qing],
Green, B.[Bradley],
Shapira, L.[Lior],
Wu, Y.[Ying],
Contrastive Learning for Label Efficient Semantic Segmentation,
ICCV21(10603-10613)
IEEE DOI
2203
Training, Image segmentation, Annotations, Semantics, Training data,
Performance gain, Representation learning,
grouping and shape
BibRef
van Gansbeke, W.[Wouter],
Vandenhende, S.[Simon],
Georgoulis, S.[Stamatios],
Van Gool, L.J.[Luc J.],
Unsupervised Semantic Segmentation by Contrasting Object Mask
Proposals,
ICCV21(10032-10042)
IEEE DOI
2203
Visualization, Image segmentation, Codes, Semantics,
Benchmark testing, Proposals, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Alonso, I.[Ińigo],
Sabater, A.[Alberto],
Ferstl, D.[David],
Montesano, L.[Luis],
Murillo, A.C.[Ana C.],
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive
Learning from a Class-wise Memory Bank,
ICCV21(8199-8208)
IEEE DOI
2203
Training, Codes, Semantics, Benchmark testing, Task analysis,
Transfer/Low-shot/Semi/Unsupervised Learning,
Vision for robotics and autonomous vehicles
BibRef
Yuan, J.L.[Jian-Long],
Liu, Y.F.[Yi-Fan],
Shen, C.H.[Chun-Hua],
Wang, Z.B.[Zhi-Bin],
Li, H.[Hao],
A Simple Baseline for Semi-supervised Semantic Segmentation with
Strong Data Augmentation*,
ICCV21(8209-8218)
IEEE DOI
2203
Training, Resistance, Image segmentation, Semantics,
Semisupervised learning, Labeling, grouping and shape
BibRef
Pan, Z.Y.[Zhi-Yi],
Jiang, P.[Peng],
Wang, Y.H.[Yun-Hai],
Tu, C.H.[Chang-He],
Cohn, A.G.[Anthony G.],
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on
Neural Representation and Self-Supervision on Neural Eigenspace,
ICCV21(7396-7405)
IEEE DOI
2203
Visualization, Image segmentation, Uncertainty, Graphical models,
Annotations, Semantics, Segmentation, grouping and shape,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhong, Y.Y.[Yuan-Yi],
Yuan, B.[Bodi],
Wu, H.[Hong],
Yuan, Z.Q.[Zhi-Qiang],
Peng, J.[Jian],
Wang, Y.X.[Yu-Xiong],
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation,
ICCV21(7253-7262)
IEEE DOI
2203
Training, Image segmentation, Computational modeling, Semantics,
Computer architecture, Benchmark testing, Segmentation,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Sun, K.Y.[Kun-Yang],
Shi, H.Q.[Hao-Qing],
Zhang, Z.M.[Zheng-Ming],
Huang, Y.M.[Yong-Ming],
ECS-Net: Improving Weakly Supervised Semantic Segmentation by Using
Connections Between Class Activation Maps,
ICCV21(7263-7272)
IEEE DOI
2203
Training, Location awareness, Image segmentation, Shape,
Design methodology, Semantics, Segmentation, grouping and shape,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhang, F.[Fei],
Gu, C.C.[Chao-Chen],
Zhang, C.Y.[Chen-Yue],
Dai, Y.C.[Yu-Chao],
Complementary Patch for Weakly Supervised Semantic Segmentation,
ICCV21(7222-7231)
IEEE DOI
2203
Image segmentation, Correlation, Semantics, Pipelines,
Information theory, Segmentation, grouping and shape,
BibRef
Li, Y.[Yi],
Kuang, Z.H.[Zhang-Hui],
Liu, L.Y.[Li-Yang],
Chen, Y.M.[Yi-Min],
Zhang, W.[Wayne],
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation,
ICCV21(6944-6953)
IEEE DOI
2203
Training, Image segmentation, Smoothing methods, Codes, Semantics,
Pipelines, Segmentation, grouping and shape,
BibRef
Xu, L.[Lian],
Ouyang, W.L.[Wan-Li],
Bennamoun, M.[Mohammed],
Boussaid, F.[Farid],
Sohel, F.[Ferdous],
Xu, D.[Dan],
Leveraging Auxiliary Tasks with Affinity Learning for Weakly
Supervised Semantic Segmentation,
ICCV21(6964-6973)
IEEE DOI
2203
Training, Representation learning, Learning systems,
Image segmentation, Semantics, Multitasking, Reliability,
BibRef
Kweon, H.[Hyeokjun],
Yoon, S.H.[Sung-Hoon],
Yoon, K.J.[Kuk-Jin],
Weakly Supervised Semantic Segmentation via Adversarial Learning of
Classifier and Reconstructor,
CVPR23(11329-11339)
IEEE DOI
2309
BibRef
Kweon, H.[Hyeokjun],
Kim, J.[Jihun],
Yoon, K.J.[Kuk-Jin],
Weakly Supervised Point Cloud Semantic Segmentation via Artificial
Oracle,
CVPR24(3721-3731)
IEEE DOI Code:
WWW Link.
2410
Point cloud compression, Training, Solid modeling, Annotations,
Semantic segmentation, Manuals, Point Cloud, Semantic Segmentation,
Weakly Supervised Learning
BibRef
Yoon, S.H.[Sung-Hoon],
Kweon, H.[Hyeokjun],
Cho, J.[Jegyeong],
Kim, S.[Shinjeong],
Yoon, K.J.[Kuk-Jin],
Adversarial Erasing Framework via Triplet with Gated Pyramid Pooling
Layer for Weakly Supervised Semantic Segmentation,
ECCV22(XXIX:326-344).
Springer DOI
2211
BibRef
Kweon, H.[Hyeokjun],
Yoon, S.H.[Sung-Hoon],
Kim, H.[Hyeonseong],
Park, D.[Daehee],
Yoon, K.J.[Kuk-Jin],
Unlocking the Potential of Ordinary Classifier: Class-specific
Adversarial Erasing Framework for Weakly Supervised Semantic
Segmentation,
ICCV21(6974-6983)
IEEE DOI
2203
Image segmentation, Codes, Semantics, Cams, Segmentation,
grouping and shape, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Su, Y.K.[Yu-Kun],
Sun, R.Z.[Rui-Zhou],
Lin, G.S.[Guo-Sheng],
Wu, Q.Y.[Qing-Yao],
Context Decoupling Augmentation for Weakly Supervised Semantic
Segmentation,
ICCV21(6984-6994)
IEEE DOI
2203
Training, Deep learning, Image segmentation, Image recognition,
Image color analysis, Semantics, Neural networks, Segmentation,
BibRef
He, R.F.[Rui-Fei],
Yang, J.[Jihan],
Qi, X.J.[Xiao-Juan],
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic
Segmentation: A Baseline Investigation,
ICCV21(6910-6920)
IEEE DOI
2203
Training, Codes, Semantics, Data models, Labeling, Iterative methods,
Segmentation, grouping and shape, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Maracani, A.[Andrea],
Michieli, U.[Umberto],
Toldo, M.[Marco],
Zanuttigh, P.[Pietro],
RECALL: Replay-based Continual Learning in Semantic Segmentation,
ICCV21(7006-7015)
IEEE DOI
2203
Training, Couplings, Privacy, Image segmentation, Databases, Semantics,
Segmentation, grouping and shape,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Wu, Y.[Yanran],
Li, X.T.[Xiang-Tai],
Shi, C.[Chen],
Tong, Y.H.[Yun-Hai],
Hua, Y.[Yang],
Song, T.[Tao],
Ma, R.H.[Ru-Hui],
Guan, H.B.[Hai-Bing],
Fast and Accurate Scene Parsing via Bi-Direction Alignment Networks,
ICIP21(2508-2512)
IEEE DOI
2201
Image segmentation, Semantics, Bidirectional control, Logic gates,
Spatial resolution, Bidirectional Alignment Network,
Fast and Accurate Scene Parsing
See also BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation.
BibRef
Lu, H.C.[Hong-Chao],
Wei, C.[Chao],
Deng, Z.D.[Zhi-Dong],
Learning With Memory for Few-Shot Semantic Segmentation,
ICIP21(629-633)
IEEE DOI
2201
Image segmentation, Semantics, Task analysis, Optimization,
Few-shot semantic segmentation, attention map,
memory
BibRef
Tsutsui, S.[Shungo],
Hirakawa, T.[Tsubasa],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Semantic Segmentation and Change Detection By Multi-Task U-Net,
ICIP21(619-623)
IEEE DOI
2201
Image segmentation, Semantics, Feature extraction, Decoding,
Task analysis, Change Detection, Semantic Segmentation, Multi-task Learning
BibRef
Chen, Y.[Ying],
Ouyang, X.[Xu],
Zhu, K.Y.[Kai-Yue],
Agam, G.[Gady],
ComplexMix: Semi-Supervised Semantic Segmentation Via Mask-Based Data
Augmentation,
ICIP21(2264-2268)
IEEE DOI
2201
Training, Image segmentation, Image analysis, Semantics,
Training data, Production, Semisupervised learning,
ComplexMix
BibRef
Yang, B.[Biao],
Xue, F.G.[Fan-Ghui],
Qi, Y.Y.[Ying-Yong],
Xin, J.[Jack],
Improving Efficient Semantic Segmentation Networks by Enhancing
Multi-scale Feature Representation via Resolution Path Based Knowledge
Distillation and Pixel Shuffle,
ISVC21(II:325-336).
Springer DOI
2112
BibRef
Zhu, L.Y.[Lan-Yun],
Ji, D.Y.[De-Yi],
Zhu, S.P.[Shi-Ping],
Gan, W.H.[Wei-Hao],
Wu, W.[Wei],
Yan, J.J.[Jun-Jie],
Learning Statistical Texture for Semantic Segmentation,
CVPR21(12532-12541)
IEEE DOI
2111
Image segmentation, Technological innovation,
Quantization (signal), Semantics, Benchmark testing, Feature extraction
BibRef
Das, A.[Anurag],
Xian, Y.Q.[Yong-Qin],
Dai, D.X.[Deng-Xin],
Schiele, B.[Bernt],
Weakly-Supervised Domain Adaptive Semantic Segmentation with
Prototypical Contrastive Learning,
CVPR23(15434-15443)
IEEE DOI
2309
BibRef
Gong, R.[Rui],
Chen, Y.H.[Yu-Hua],
Paudel, D.P.[Danda Pani],
Li, Y.[Yawei],
Chhatkuli, A.[Ajad],
Li, W.[Wen],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound
Domain Adaptive Semantic Segmentation,
CVPR21(8340-8350)
IEEE DOI
2111
Adaptation models, Image segmentation, Fuses,
Computational modeling, Semantics, Benchmark testing, Prediction algorithms
BibRef
Douillard, A.[Arthur],
Chen, Y.[Yifu],
Dapogny, A.[Arnaud],
Cord, M.[Matthieu],
PLOP: Learning without Forgetting for Continual Semantic Segmentation,
CVPR21(4039-4049)
IEEE DOI
2111
Learning systems, Semantics, Benchmark testing,
Predictive models, Stability analysis
BibRef
Zhao, Z.[Zhen],
Long, S.F.[Si-Fan],
Pi, J.[Jimin],
Wang, J.D.[Jing-Dong],
Zhou, L.P.[Lu-Ping],
Instance-Specific and Model-Adaptive Supervision for Semi-Supervised
Semantic Segmentation,
CVPR23(23705-23714)
IEEE DOI
2309
BibRef
Li, S.[Shuo],
He, Y.[Yue],
Zhang, W.M.[Wei-Ming],
Zhang, W.[Wei],
Tan, X.[Xiao],
Han, J.Y.[Jun-Yu],
Ding, E.[Errui],
Wang, J.D.[Jing-Dong],
CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and
Contour Guidance Supervision,
ICCV23(16302-16312)
IEEE DOI
2401
BibRef
Chen, X.K.[Xiao-Kang],
Yuan, Y.H.[Yu-Hui],
Zeng, G.[Gang],
Wang, J.D.[Jing-Dong],
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision,
CVPR21(2613-2622)
IEEE DOI
2111
Training, Image segmentation, Semantics,
Training data, Standards
BibRef
Yao, Y.Z.[Ya-Zhou],
Chen, T.[Tao],
Xie, G.S.[Guo-Sen],
Zhang, C.Y.[Chuan-Yi],
Shen, F.M.[Fu-Min],
Wu, Q.[Qi],
Tang, Z.M.[Zhen-Min],
Zhang, J.[Jian],
Non-Salient Region Object Mining for Weakly Supervised Semantic
Segmentation,
CVPR21(2623-2632)
IEEE DOI
2111
Image segmentation, Codes, Annotations,
Image edge detection, Semantics, Cognition
BibRef
Araslanov, N.[Nikita],
Roth, S.[Stefan],
Self-supervised Augmentation Consistency for Adapting Semantic
Segmentation,
CVPR21(15379-15389)
IEEE DOI
2111
Training, Image segmentation, Semantics,
Computer architecture, Complexity theory
BibRef
Liu, B.H.[Bing-Hao],
Ding, Y.[Yao],
Jiao, J.B.[Jian-Bin],
Ji, X.Y.[Xiang-Yang],
Ye, Q.[Qixiang],
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic
Segmentation,
CVPR21(9742-9751)
IEEE DOI
2111
Training, Support vector machines, Systematics,
Codes, Semantics, Training data
BibRef
He, W.[Wei],
Wu, M.Q.[Mei-Qing],
Liang, M.F.[Ming-Fu],
Lam, S.K.[Siew-Kei],
CAP: Context-Aware Pruning for Semantic Segmentation,
WACV21(959-968)
IEEE DOI
2106
Adaptation models, Image segmentation, Quantization (signal),
Semantics, Redundancy, Benchmark testing
BibRef
Toldo, M.[Marco],
Michieli, U.[Umberto],
Zanuttigh, P.[Pietro],
Unsupervised Domain Adaptation in Semantic Segmentation via
Orthogonal and Clustered Embeddings,
WACV21(1357-1367)
IEEE DOI
2106
Knowledge engineering, Deep learning, Clustering methods, Semantics, Standards
BibRef
Wang, Z.Y.[Zhuo-Ying],
Wang, Y.T.[Yong-Tao],
Tang, Z.[Zhi],
Li, Y.Y.[Yang-Yan],
Chen, Y.[Ying],
Ling, H.B.[Hai-Bin],
Lin, W.S.[Wei-Si],
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning
in Semantic Segmentation,
ICPR21(7111-7118)
IEEE DOI
2105
Lips, Semantics, Computer architecture, Logic gates,
Benchmark testing, Feature extraction, Computational efficiency
BibRef
Li, Y.J.[Ya-Jun],
Liu, Y.Z.[Ya-Zhou],
Sun, Q.S.[Quan-Sen],
Real-time Semantic Segmentation via Region and Pixel Context Network,
ICPR21(7043-7049)
IEEE DOI
2105
Semantics, Graphics processing units, Real-time systems,
Task analysis, Real-time,
Location attention
BibRef
Ullah, I.[Ihsan],
Reilly, S.[Sean],
Madden, M.G.[Michael G.],
Enhancing Semantic Segmentation of Aerial Images with Inhibitory
Neurons,
ICPR21(5451-5458)
IEEE DOI
2105
Training, Deep learning, Image segmentation, Neurons, Semantics,
Transfer learning, Training data
BibRef
Terreran, M.[Matteo],
Bonetto, E.[Elia],
Ghidoni, S.[Stefano],
Enhancing Deep Semantic Segmentation of RGB-D Data with Entangled
Forests,
ICPR21(4634-4641)
IEEE DOI
2105
Deep learning, Computational modeling, Semantics
BibRef
Zhao, N.[Na],
Chua, T.S.[Tat-Seng],
Lee, G.H.[Gim Hee],
PS2-Net: A Locally and Globally Aware Network for Point-Based
Semantic Segmentation,
ICPR21(723-730)
IEEE DOI
2105
Deep learning, Solid modeling,
Semantics, Neural networks, Task analysis
BibRef
Shan, L.L.[Lian-Lei],
Li, M.L.[Ming-Long],
Li, X.O.[Xia-Obin],
Bai, Y.[Yang],
Lv, K.[Ke],
Luo, B.[Bin],
Chen, S.B.[Si-Bao],
Wang, W.Q.[Wei-Qiang],
UHRSNet: A Semantic Segmentation Network Specifically for
Ultra-High-Resolution Images,
ICPR21(1460-1466)
IEEE DOI
2105
Training, Image segmentation, Semantics,
Graphics processing units, Feature extraction, Pattern recognition
BibRef
Ye, M.C.[Mu-Cong],
Ouyang, J.P.[Jing-Peng],
Chen, G.[Ge],
Zhang, J.[Jing],
Yu, X.G.[Xiao-Gang],
Enhanced Feature Pyramid Network for Semantic Segmentation,
ICPR21(3209-3216)
IEEE DOI
2105
Visualization, Semantics, Object detection, Benchmark testing,
Feature extraction, Robustness, Decoding
BibRef
Gan, Y.[Yi],
Xu, W.[Wei],
Su, J.B.[Jian-Bo],
SFPN: Semantic Feature Pyramid Network for Object Detection,
ICPR21(795-802)
IEEE DOI
2105
BibRef
And: A2, A1, A3:
Bidirectional Matrix Feature Pyramid Network for Object Detection,
ICPR21(8000-8007)
IEEE DOI
2105
Semantics, Object detection, Detectors, Feature extraction,
Task analysis, object detection, semantic segmentation.
Location awareness, Fuses, Detectors,
Feature extraction, Time division multiplexing
BibRef
Courdier, E.[Evann],
Fleuret, F.[François],
Real-time Segmentation Networks Should be Latency Aware,
ACCV20(I:603-619).
Springer DOI
2103
BibRef
Yang, X.N.[Xin-Neng],
Wu, Y.[Yan],
Zhao, J.Q.[Jun-Qiao],
Liu, F.L.[Fei-Lin],
Dense Dual-path Network for Real-time Semantic Segmentation,
ACCV20(I:553-570).
Springer DOI
2103
BibRef
Qu, L.Z.[Lin-Zi],
He, L.H.[Li-Huo],
Ke, J.J.[Jun-Ji],
Gao, X.B.[Xin-Bo],
Lu, W.[Wen],
Learning More Accurate Features for Semantic Segmentation in Cyclenet,
ACCV20(I:571-584).
Springer DOI
2103
BibRef
Xie, S.[Shuai],
Feng, Z.[Zunlei],
Chen, Y.[Ying],
Sun, S.T.[Song-Tao],
Ma, C.[Chao],
Song, M.L.[Ming-Li],
Deal: Difficulty-aware Active Learning for Semantic Segmentation,
ACCV20(I:672-688).
Springer DOI
2103
BibRef
Peters, T.,
Brenner, C.,
Song, M.,
Improving Deep Learning Based Semantic Segmentation with Multi View
Outlier Correction,
ISPRS20(B2:711-716).
DOI Link
2012
BibRef
Koh, J.Y.[Jing Yu],
Nguyen, D.T.[Duc Thanh],
Truong, Q.T.[Quang-Trung],
Yeung, S.K.[Sai-Kit],
Binder, A.[Alexander],
Sideinfnet: A Deep Neural Network for Semi-automatic Semantic
Segmentation with Side Information,
ECCV20(XXIV:103-118).
Springer DOI
2012
BibRef
Wang, H.C.[Hao-Chen],
Zhang, X.D.[Xu-Dong],
Hu, Y.T.[Yu-Tao],
Yang, Y.[Yandan],
Cao, X.B.[Xian-Bin],
Zhen, X.T.[Xian-Tong],
Few-shot Semantic Segmentation with Democratic Attention Networks,
ECCV20(XI:730-746).
Springer DOI
2011
BibRef
Liu, Q.,
El-Khamy, M.,
Bai, D.,
Lee, J.,
GSANet: Semantic Segmentation With Global And Selective Attention,
ICIP20(1471-1475)
IEEE DOI
2011
Feature extraction, Semantics, Decoding, Image segmentation,
Aggregates, Data mining, Benchmark testing, GSANet, sparsemax
BibRef
Wang, H.,
Yang, Y.,
Jiang, X.,
Cao, X.,
Zhen, X.,
You Only Need The Image: Unsupervised Few-Shot Semantic Segmentation
With Co-Guidance Network,
ICIP20(1496-1500)
IEEE DOI
2011
Image segmentation, Training, Semantics, Feature extraction,
Decoding, Testing, Measurement, Few-shot, Unsupervised,
Co-guidance
BibRef
Soliman, M.,
Lehman, C.,
Al Regib, G.,
S6: Semi-Supervised Self-Supervised Semantic Segmentation,
ICIP20(1861-1865)
IEEE DOI
2011
Image reconstruction, Image segmentation, Task analysis,
Data models, Decoding, Training, Semisupervised learning,
Image Reconstruction.
BibRef
Sheshkus, A.,
Nikolaev, D.,
Arlazarov, V.L.,
Houghencoder: Neural Network Architecture for Document Image Semantic
Segmentation,
ICIP20(1946-1950)
IEEE DOI
2011
Neural networks, Transforms, Computer architecture, Task analysis,
Image segmentation, Semantics, Training, Semantic segmentation,
Fast Hough Transform
BibRef
Jiang, J.,
Liu, J.,
Fu, J.,
Zhu, X.,
Lu, H.,
Point Set Attention Network For Semantic Segmentation,
ICIP20(2186-2190)
IEEE DOI
2011
Noise measurement, Semantics, Image segmentation, Aggregates,
Context modeling, Training, Task analysis,
Self-attention
BibRef
Isaacs, O.[Or],
Shayer, O.[Oran],
Lindenbaum, M.[Michael],
Enhancing Generic Segmentation With Learned Region Representations,
CVPR20(12943-12952)
IEEE DOI
2008
Image segmentation, Task analysis, Image edge detection, Training,
Semantics, Face, Image color analysis
BibRef
Ouali, Y.[Yassine],
Hudelot, C.[Céline],
Tami, M.[Myriam],
Autoregressive Unsupervised Image Segmentation,
ECCV20(VII:142-158).
Springer DOI
2011
BibRef
And:
Semi-Supervised Semantic Segmentation With Cross-Consistency Training,
CVPR20(12671-12681)
IEEE DOI
2008
Decoding, Training, Perturbation methods, Semantics,
Image segmentation, Predictive models, Task analysis
BibRef
Ibrahim, M.S.,
Vahdat, A.,
Ranjbar, M.,
Macready, W.G.,
Semi-Supervised Semantic Image Segmentation With Self-Correcting
Networks,
CVPR20(12712-12722)
IEEE DOI
2008
Image segmentation, Training, Semantics, Predictive models, Decoding,
Noise measurement, Robustness
BibRef
Kim, M.,
Byun, H.,
Learning Texture Invariant Representation for Domain Adaptation of
Semantic Segmentation,
CVPR20(12972-12981)
IEEE DOI
2008
Adaptation models, Image segmentation, Semantics, Training,
Visualization, Predictive models, Task analysis
BibRef
Klingner, M.,
Bär, A.,
Fingscheidt, T.,
Improved Noise and Attack Robustness for Semantic Segmentation by
Using Multi-Task Training with Self-Supervised Depth Estimation,
SAIAD20(1299-1309)
IEEE DOI
2008
Semantics, Robustness, Training, Estimation, Image segmentation,
Task analysis, Perturbation methods
BibRef
Wang, Z.,
Yu, M.,
Wei, Y.,
Feris, R.,
Xiong, J.,
Hwu, W.,
Huang, T.S.,
Shi, H.,
Differential Treatment for Stuff and Things: A Simple Unsupervised
Domain Adaptation Method for Semantic Segmentation,
CVPR20(12632-12641)
IEEE DOI
2008
Feature extraction, Semantics, Task analysis, Training,
Image segmentation, Adaptation models, Generators
BibRef
Iqbal, J.,
Ali, M.,
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with
Spatially Independent and Semantically Consistent Labeling,
WACV20(1853-1862)
IEEE DOI
2006
Semantics, Image segmentation, Adaptation models, Training,
Computational modeling, Task analysis, Roads
BibRef
Wu, Y.,
Jiang, A.,
Tang, Y.,
Kwan, H.K.,
GRNet: Deep Convolutional Neural Networks based on Graph Reasoning
for Semantic Segmentation,
VCIP20(116-119)
IEEE DOI
2102
Semantics, Convolution, Cognition, Image segmentation,
Feature extraction, Training, Network architecture,
semantic segmentation
BibRef
Zhang, B.,
Zhao, S.,
Zhang, R.,
Towards Adaptive Semantic Segmentation By Progressive Feature
Refinement,
ICIP20(2221-2225)
IEEE DOI
2011
Image segmentation, Semantics, Task analysis, Adaptation models,
Machine learning, Computational modeling, Feature extraction,
deep learning
BibRef
Li, Z.,
Bao, W.,
Zheng, J.,
Xu, C.,
Deep Grouping Model for Unified Perceptual Parsing,
CVPR20(4052-4062)
IEEE DOI
2008
Semantics, Task analysis, Computational modeling,
Image segmentation, Adaptation models, Context modeling, Message passing
BibRef
Saha, S.[Sudipan],
Sudhakaran, S.[Swathikiran],
Banerjee, B.[Biplab],
Pendurkar, S.[Sumedh],
Semantic Guided Deep Unsupervised Image Segmentation,
CIAP19(II:499-510).
Springer DOI
1909
BibRef
Li, X.,
Zhong, Z.,
Wu, J.,
Yang, Y.,
Lin, Z.,
Liu, H.,
Expectation-Maximization Attention Networks for Semantic Segmentation,
ICCV19(9166-9175)
IEEE DOI
2004
expectation-maximisation algorithm,
feature extraction, image representation, image segmentation, Convergence
BibRef
Zou, Y.,
Yu, Z.,
Liu, X.,
Kumar, B.V.K.V.,
Wang, J.,
Confidence Regularized Self-Training,
ICCV19(5981-5990)
IEEE DOI
2004
Code, Segmentation.
WWW Link. image classification, image segmentation, iterative methods,
unsupervised learning, pseudolabels, overconfident label belief, Semantics
BibRef
Fu, J.,
Liu, J.,
Wang, Y.,
Li, Y.,
Bao, Y.,
Tang, J.,
Lu, H.,
Adaptive Context Network for Scene Parsing,
ICCV19(6747-6756)
IEEE DOI
2004
feature extraction, image segmentation, neural nets,
context module, adaptive contextual features, Semantics
BibRef
Lv, F.M.[Feng-Mao],
Liang, T.[Tao],
Chen, X.[Xiang],
Lin, G.S.[Guo-Sheng],
Cross-Domain Semantic Segmentation via Domain-Invariant Interactive
Relation Transfer,
CVPR20(4333-4342)
IEEE DOI
2008
Image segmentation, Semantics, Adaptation models, Training,
Image reconstruction, Neural networks, Biological system modeling
BibRef
Lian, Q.[Qing],
Duan, L.X.[Li-Xin],
Lv, F.M.[Feng-Mao],
Gong, B.Q.[Bo-Qing],
Constructing Self-Motivated Pyramid Curriculums for Cross-Domain
Semantic Segmentation: A Non-Adversarial Approach,
ICCV19(6757-6766)
IEEE DOI
2004
generalisation (artificial intelligence), image segmentation,
neural nets, unsupervised learning, generalization capability, Logistics
BibRef
Zhang, F.,
Chen, Y.,
Li, Z.,
Hong, Z.,
Liu, J.,
Ma, F.,
Han, J.,
Ding, E.,
ACFNet: Attentional Class Feature Network for Semantic Segmentation,
ICCV19(6797-6806)
IEEE DOI
2004
image representation, image segmentation, ACFNet,
semantic segmentation, spatial perspective, Training
BibRef
Lee, J.,
Kim, E.,
Lee, S.,
Lee, J.,
Yoon, S.,
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly
Supervised Semantic Segmentation,
ICCV19(6807-6817)
IEEE DOI
2004
image representation, image segmentation, image sequences,
Internet, neural nets, object detection, supervised learning,
Object recognition
BibRef
Zhong, C.Y.[Chang-Yuan],
Hu, Z.L.[Ze-Lin],
Li, M.[Miao],
Li, H.L.[Hua-Long],
Yang, X.J.[Xuan-Jiang],
Liu, F.[Fei],
Dual Stream Segmentation Network for Real-Time Semantic Segmentation,
ICIVC20(144-149)
IEEE DOI
2009
Semantics, Real-time systems, Streaming media, Image segmentation,
Spatial resolution, Computer architecture, Feature extraction,
TwoBranch Framework
BibRef
Li, Y.,
Song, L.,
Chen, Y.,
Li, Z.,
Zhang, X.,
Wang, X.,
Sun, J.,
Learning Dynamic Routing for Semantic Segmentation,
CVPR20(8550-8559)
IEEE DOI
2008
Routing, Semantics, Computer architecture, Network architecture,
Logic gates, Dynamic scheduling
BibRef
Bar, A.,
Klingner, M.,
Varghese, S.,
Hüger, F.,
Schlicht, P.,
Fingscheidt, T.,
Robust Semantic Segmentation by Redundant Networks With a
Layer-Specific Loss Contribution and Majority Vote,
SAIAD20(1348-1358)
IEEE DOI
2008
Robustness, Semantics, Task analysis, Adaptive systems,
Image segmentation, Training, Neural networks
BibRef
Pavlitskaya, S.,
Hubschneider, C.,
Weber, M.,
Moritz, R.,
Hüger, F.,
Schlicht, P.,
Zöllner, J.M.,
Using Mixture of Expert Models to Gain Insights into Semantic
Segmentation,
SAIAD20(1399-1406)
IEEE DOI
2008
Logic gates, Computer architecture, Feature extraction,
Uncertainty, Semantics, Neural networks, Task analysis
BibRef
Yuan, J.,
Deng, Z.,
Wang, S.,
Luo, Z.,
Multi Receptive Field Network for Semantic Segmentation,
WACV20(1883-1892)
IEEE DOI
2006
Image edge detection, Semantics, Task analysis, Image segmentation,
Training, Feature extraction, Standards
BibRef
Zhang, C.,
Lin, G.,
Liu, F.,
Guo, J.,
Wu, Q.,
Yao, R.,
Pyramid Graph Networks With Connection Attentions for Region-Based
One-Shot Semantic Segmentation,
ICCV19(9586-9594)
IEEE DOI
2004
data structures, graph theory, image representation,
image segmentation, message passing, data representations,
BibRef
Xu, Y.J.[Ya-Jun],
Mao, Z.D.[Zhen-Dong],
Zhang, P.[Peng],
Wang, B.[Bin],
Compact Position-aware Attention Network for Image Semantic
Segmentation,
MMMod20(II:639-650).
Springer DOI
2003
BibRef
Jain, S.[Samvit],
Wang, X.[Xin],
Gonzalez, J.E.[Joseph E.],
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation
on Video,
CVPR19(8858-8867).
IEEE DOI
2002
BibRef
Fu, J.[Jun],
Liu, J.[Jing],
Tian, H.[Haijie],
Li, Y.[Yong],
Bao, Y.J.[Yong-Jun],
Fang, Z.W.[Zhi-Wei],
Lu, H.Q.[Han-Qing],
Dual Attention Network for Scene Segmentation,
CVPR19(3141-3149).
IEEE DOI
2002
BibRef
Wang, K.,
Wang, C.,
Tai, T.,
Wang, J.,
Object Bounding Transformed Network for End-to-End Semantic
Segmentation,
ICIP19(3217-3221)
IEEE DOI
1910
image semantic segmentation, Object Boundary Guide,
Doman Transform Network, ResNet 101
BibRef
Pham, Q.,
Hua, B.,
Nguyen, T.,
Yeung, S.,
Real-Time Progressive 3D Semantic Segmentation for Indoor Scenes,
WACV19(1089-1098)
IEEE DOI
1904
image reconstruction, image segmentation, time progressive 3D,
widespread adoption, autonomous systems, assistant robots,
Neural networks
BibRef
Wu, Z.,
Wang, X.,
Gonzalez, J.,
Goldstein, T.,
Davis, L.S.,
ACE: Adapting to Changing Environments for Semantic Segmentation,
ICCV19(2121-2130)
IEEE DOI
2004
gradient methods, image segmentation,
learning (artificial intelligence), neural nets, Lighting
BibRef
Du, L.,
Tan, J.,
Yang, H.,
Feng, J.,
Xue, X.,
Zheng, Q.,
Ye, X.,
Zhang, X.,
SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network
for Semantic Segmentation,
ICCV19(982-991)
IEEE DOI
2004
feature extraction, image segmentation,
learning (artificial intelligence), semantic segmentation, Data models
BibRef
Zhu, Z.,
Xu, M.,
Bai, S.,
Huang, T.,
Bai, X.,
Asymmetric Non-Local Neural Networks for Semantic Segmentation,
ICCV19(593-602)
IEEE DOI
2004
Code, Segmentation.
WWW Link. image fusion, image segmentation, neural nets,
asymmetric nonlocal neural networks, Semantics
BibRef
Huang, Z.,
Wang, X.,
Huang, L.,
Huang, C.,
Wei, Y.,
Liu, W.,
CCNet: Criss-Cross Attention for Semantic Segmentation,
ICCV19(603-612)
IEEE DOI
2004
Code, Segmentation.
WWW Link. image segmentation, information retrieval,
learning (artificial intelligence), recurrent neural nets, Complexity theory
BibRef
Kato, N.,
Yamasaki, T.,
Aizawa, K.,
Zero-Shot Semantic Segmentation via Variational Mapping,
MDALC19(1363-1370)
IEEE DOI
2004
image segmentation, learning (artificial intelligence),
neural nets, object detection, object recognition,
semantic segmentation
BibRef
Shaw, A.,
Hunter, D.,
Landola, F.,
Sidhu, S.,
SqueezeNAS: Fast Neural Architecture Search for Faster Semantic
Segmentation,
NeruArch19(2014-2024)
IEEE DOI
2004
image classification, image segmentation,
neural net architecture, optimisation, parallel processing,
Deep Learning
BibRef
Zhuang, J.,
Yang, J.,
Gu, L.,
Dvornek, N.,
ShelfNet for Fast Semantic Segmentation,
CVRSUAD19(847-856)
IEEE DOI
2004
Code, Segmentation.
WWW Link. image segmentation, image understanding, semantic segmentation,
PASCAL VOC dataset, PSPNet, ResNet34 backbone, ShelfNet,
Realtime
BibRef
Zhang, Y.H.[Yi-Heng],
Qiu, Z.F.[Zhao-Fan],
Liu, J.G.[Jin-Gen],
Yao, T.[Ting],
Liu, D.[Dong],
Mei, T.[Tao],
Customizable Architecture Search for Semantic Segmentation,
CVPR19(11633-11642).
IEEE DOI
2002
BibRef
Nekrasov, V.[Vladimir],
Chen, H.[Hao],
Shen, C.H.[Chun-Hua],
Reid, I.D.[Ian D.],
Fast Neural Architecture Search of Compact Semantic Segmentation Models
via Auxiliary Cells,
CVPR19(9118-9127).
IEEE DOI
2002
BibRef
Larsson, M.[Mans],
Stenborg, E.[Erik],
Hammarstrand, L.[Lars],
Pollefeys, M.[Marc],
Sattler, T.[Torsten],
Kahl, F.[Fredrik],
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation,
CVPR19(9524-9534).
IEEE DOI
2002
BibRef
Tokunaga, H.[Hiroki],
Teramoto, Y.[Yuki],
Yoshizawa, A.[Akihiko],
Bise, R.[Ryoma],
Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in
Pathology,
CVPR19(12589-12598).
IEEE DOI
2002
BibRef
Li, Y.S.[Yun-Sheng],
Yuan, L.[Lu],
Vasconcelos, N.M.[Nuno M.],
Bidirectional Learning for Domain Adaptation of Semantic Segmentation,
CVPR19(6929-6938).
IEEE DOI
2002
BibRef
Wei, Z.[Zhen],
Zhang, J.Y.[Jing-Yi],
Liu, L.[Li],
Zhu, F.[Fan],
Shen, F.M.[Fu-Min],
Zhou, Y.[Yi],
Liu, S.[Si],
Sun, Y.[Yao],
Shao, L.[Ling],
Building Detail-Sensitive Semantic Segmentation Networks With
Polynomial Pooling,
CVPR19(7108-7116).
IEEE DOI
2002
BibRef
He, J.J.[Jun-Jun],
Deng, Z.Y.[Zhong-Ying],
Zhou, L.[Lei],
Wang, Y.[Yali],
Qiao, Y.[Yu],
Adaptive Pyramid Context Network for Semantic Segmentation,
CVPR19(7511-7520).
IEEE DOI
2002
BibRef
Li, H.[Hanchao],
Xiong, P.F.[Peng-Fei],
Fan, H.Q.A.[Hao-Qi-Ang],
Sun, J.[Jian],
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation,
CVPR19(9514-9523).
IEEE DOI
2002
BibRef
Sun, R.Q.[Ruo-Qi],
Zhu, X.G.[Xin-Ge],
Wu, C.R.[Chong-Ruo],
Huang, C.[Chen],
Shi, J.P.[Jian-Ping],
Ma, L.Z.[Li-Zhuang],
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation
via Hierarchical Region Selection,
CVPR19(4355-4364).
IEEE DOI
2002
BibRef
Chen, Y.H.[Yu-Hua],
Li, W.[Wen],
Chen, X.[Xiaoran],
Van Gool, L.J.[Luc J.],
Learning Semantic Segmentation From Synthetic Data: A Geometrically
Guided Input-Output Adaptation Approach,
CVPR19(1841-1850).
IEEE DOI
2002
BibRef
Lyu, H.,
Fu, H.,
Hu, X.,
Liu, L.,
Esnet: Edge-Based Segmentation Network for Real-Time Semantic
Segmentation in Traffic Scenes,
ICIP19(1855-1859)
IEEE DOI
1910
Real-Time, Semantic Segmentation, Global Edge Information,
Classification Level Semantic Information
BibRef
Liu, M.,
Yin, H.,
Cross Attention Network for Semantic Segmentation,
ICIP19(2434-2438)
IEEE DOI
1910
Semantic segmentation, cross attention, real-time, deep neural networks
BibRef
Guo, J.,
Markoni, H.,
Image Semantic Segmentation With Edge and Feature Level Attenuators,
ICIP19(2511-2515)
IEEE DOI
1910
ENet, skip connection, attenuator, edge selector, image segmentation
BibRef
Ganti, P.,
Waslander, S.L.,
Network Uncertainty Informed Semantic Feature Selection for Visual
SLAM,
CRV19(121-128)
IEEE DOI
1908
Simultaneous localization and mapping, Feature extraction,
Uncertainty, Artificial neural networks, Semantics, Entropy,
Semantic Segmentation
BibRef
Xiang, W.,
Mao, H.,
Athitsos, V.,
ThunderNet:
A Turbo Unified Network for Real-Time Semantic Segmentation,
WACV19(1789-1796)
IEEE DOI
1904
embedded systems, graphics processing units, image segmentation,
neural nets, Turbo Unified Network, ThunderNet,
Standards
BibRef
Tang, M.,
Djelouah, A.,
Perazzi, F.,
Boykov, Y.Y.,
Schroers, C.,
Normalized Cut Loss for Weakly-Supervised CNN Segmentation,
CVPR18(1818-1827)
IEEE DOI
1812
Image segmentation, Training, Proposals, Standards,
Semisupervised learning, Entropy, Semantics
BibRef
Ye, L.,
Liu, Z.,
Wang, Y.,
Learning Semantic Segmentation with Diverse Supervision,
WACV18(1461-1469)
IEEE DOI
1806
feedforward neural nets, image classification,
image segmentation, learning (artificial intelligence),
Training
BibRef
Ortiz, A.,
Granados, A.,
Fuentes, O.,
Kiekintveld, C.,
Rosario, D.,
Bell, Z.,
Integrated Learning and Feature Selection for Deep Neural Networks in
Multispectral Images,
PBVS18(1277-127709)
IEEE DOI
1812
Image segmentation, Semantics, Machine learning, Training,
Task analysis, Feature extraction, Neural networks
BibRef
McIntosh, L.,
Maheswaranathan, N.,
Sussillo, D.,
Shlens, J.,
Recurrent Segmentation for Variable Computational Budgets,
EfficientDeep18(1729-172909)
IEEE DOI
1812
Image segmentation, Semantics, Computer architecture,
Computational efficiency, Videos, Computational modeling, Network architecture
BibRef
Sankaranarayanan, S.,
Balaji, Y.,
Jain, A.,
Lim, S.N.,
Chellappa, R.,
Learning from Synthetic Data:
Addressing Domain Shift for Semantic Segmentation,
CVPR18(3752-3761)
IEEE DOI
1812
Task analysis, Semantics, Training, Image reconstruction, Generators,
Image segmentation, Data models
BibRef
Wang, X.[Xiang],
You, S.D.[Shao-Di],
Li, X.[Xi],
Ma, H.M.[Hui-Min],
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common
Object Features,
CVPR18(1354-1362)
IEEE DOI
1812
Image segmentation, Semantics, Training, Heating systems,
Task analysis, Feature extraction
BibRef
Shen, T.,
Lin, G.,
Shen, C.,
Reid, I.D.,
Bootstrapping the Performance of Webly Supervised Semantic
Segmentation,
CVPR18(1363-1371)
IEEE DOI
1812
Training, Image segmentation, Semantics, Knowledge engineering,
Noise measurement, Bidirectional control, Estimation
BibRef
Zhang, Z.,
Xie, C.,
Wang, J.,
Xie, L.,
Yuille, A.L.,
DeepVoting: A Robust and Explainable Deep Network for Semantic Part
Detection Under Partial Occlusion,
CVPR18(1372-1380)
IEEE DOI
1812
Semantics, Visualization, Training, Wheels, Proposals,
Feature extraction, Object detection
BibRef
Zlateski, A.,
Jaroensri, R.,
Sharma, P.,
Durand, F.,
On the Importance of Label Quality for Semantic Segmentation,
CVPR18(1479-1487)
IEEE DOI
1812
Pattern recognition
BibRef
Saleh, F.S.[Fatemeh Sadat],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation,
ECCV18(II: 86-103).
Springer DOI
1810
BibRef
Huang, Q.,
Xia, C.,
Li, S.,
Wang, Y.,
Song, Y.,
Kuo, C.C.J.,
Unsupervised Clustering Guided Semantic Segmentation,
WACV18(1489-1498)
IEEE DOI
1806
feature extraction, feedforward neural nets,
image classification, image representation, image segmentation,
Training
BibRef
Nigam, I.,
Huang, C.,
Ramanan, D.,
Ensemble Knowledge Transfer for Semantic Segmentation,
WACV18(1499-1508)
IEEE DOI
1806
image segmentation, learning (artificial intelligence),
aerial drone robotics, aerial scenes, aerial segmentation,
Visualization
BibRef
Liang, X.D.[Xiao-Dan],
Xing, E.[Eric],
Zhou, H.F.[Hong-Fei],
Dynamic-Structured Semantic Propagation Network,
CVPR18(752-761)
IEEE DOI
1812
Semantics, Neurons, Task analysis, Image segmentation, Vocabulary,
Training, Correlation
BibRef
Pohlen, T.,
Hermans, A.[Alexander],
Mathias, M.,
Leibe, B.[Bastian],
Full-Resolution Residual Networks for Semantic Segmentation in Street
Scenes,
CVPR17(3309-3318)
IEEE DOI
1711
Computer architecture, Image segmentation, Network architecture,
Semantics, Streaming media, Training
BibRef
Qi, X.,
Liao, R.,
Jia, J.,
Fidler, S.,
Urtasun, R.,
3D Graph Neural Networks for RGBD Semantic Segmentation,
ICCV17(5209-5218)
IEEE DOI
1802
feature extraction, graph theory, image classification,
image representation, image segmentation,
BibRef
Sickert, S.[Sven],
Denzler, J.[Joachim],
Semantic Segmentation of Outdoor Areas Using 3D Moment Invariants and
Contextual Cues,
GCPR17(165-176).
Springer DOI
1711
BibRef
Nguyen, K.[Khoi],
Todorovic, S.[Sinisa],
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty
Estimation,
ICCV21(7376-7385)
IEEE DOI
2203
Training, Image segmentation, Uncertainty, Estimation, Task analysis,
Standards, Segmentation, grouping and shape,
BibRef
Roy, A.[Anirban],
Todorovic, S.[Sinisa],
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly
Supervised Image Segmentation,
CVPR17(7282-7291)
IEEE DOI
1711
BibRef
Earlier:
A Multi-scale CNN for Affordance Segmentation in RGB Images,
ECCV16(IV: 186-201).
Springer DOI
1611
Gaussian distribution, Image segmentation, Labeling, Neurons,
Semantics, Training, Visualization
BibRef
Schneider, L.[Lukas],
Jasch, M.[Manuel],
Fröhlich, B.[Björn],
Weber, T.[Thomas],
Franke, U.[Uwe],
Pollefeys, M.[Marc],
Rätsch, M.[Matthias],
Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object
Detection,
SCIA17(I: 98-109).
Springer DOI
1706
BibRef
Zhang, Y.[Yu],
Ngan, K.N.[King Ngi],
Huynh, C.P.[Cong Phuoc],
Habili, N.[Nariman],
Learning Deep Spatial-Spectral Features for Material Segmentation in
Hyperspectral Images,
DICTA17(1-7)
IEEE DOI
1804
feature extraction, geophysical image processing,
image classification, image segmentation,
Training
BibRef
Luo, P.[Ping],
Wang, G.R.[Guang-Run],
Lin, L.[Liang],
Wang, X.G.[Xiao-Gang],
Deep Dual Learning for Semantic Image Segmentation,
ICCV17(2737-2745)
IEEE DOI
1802
BibRef
Earlier: A2, A1, A3, A4:
Learning Object Interactions and Descriptions for Semantic Image
Segmentation,
CVPR17(5235-5243)
IEEE DOI
1711
image reconstruction, image segmentation,
learning (artificial intelligence), neural nets, DIS,
Cleaning, Cows, Feature extraction, Image segmentation, Semantics,
Streaming media.
BibRef
García, G.M.,
Husain, F.,
Schulz, H.,
Frintrop, S.,
Torras, C.,
Behnke, S.,
Semantic segmentation priors for object discovery,
ICPR16(549-554)
IEEE DOI
1705
Image color analysis, Image segmentation,
Neural networks, Proposals, Semantics
BibRef
Visin, F.,
Romero, A.,
Cho, K.,
Matteucci, M.,
Ciccone, M.,
Kastner, K.,
Bengio, Y.,
Courville, A.,
ReSeg: A Recurrent Neural Network-Based Model for Semantic
Segmentation,
DeepLearn-C16(426-433)
IEEE DOI
1612
BibRef
Murdock, C.[Calvin],
de la Torre, F.[Fernando],
Additive Component Analysis,
CVPR17(673-681)
IEEE DOI
1711
BibRef
Earlier:
Semantic Component Analysis,
ICCV15(1484-1492)
IEEE DOI
1602
Additives, Image reconstruction, Kernel, Machine learning, Manifolds,
Optimization, Principal component analysis.
Feature extraction. decomposition of images into semantic components.
BibRef
Gidaris, S.[Spyridon],
Komodakis, N.[Nikos],
Attend Refine Repeat: Active Box Proposal Generation via In-Out
Localization,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
And:
LocNet: Improving Localization Accuracy for Object Detection,
CVPR16(789-798)
IEEE DOI
1612
BibRef
Earlier:
Object Detection via a Multi-region and Semantic Segmentation-Aware
CNN Model,
ICCV15(1134-1142)
IEEE DOI
1602
Biological system modeling
BibRef
Rota Bulo, S.[Samuel],
Kontschieder, P.[Peter],
Neural Decision Forests for Semantic Image Labelling,
CVPR14(81-88)
IEEE DOI
1409
neural network; random forest; semantic image labelling
BibRef
Qi, G.J.[Guo-Jun],
Hierarchically Gated Deep Networks for Semantic Segmentation,
CVPR16(2267-2275)
IEEE DOI
1612
BibRef
Sharma, A.[Abhishek],
Tuzel, O.[Oncel],
Jacobs, D.W.[David W.],
Deep hierarchical parsing for semantic segmentation,
CVPR15(530-538)
IEEE DOI
1510
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Convolutional Neural Networks for Semantic Segmentation, CNN .