8.3.4.3.1 Neural Networks for Semantic Segmentation

Chapter Contents (Back)
Neural Networks. Semantic Segmentation.
See also Convolutional Neural Networks for Semantic Segmentation, CNN.
See also Generative Adversarial Network, GAN, Semantic Segmentation.
See also Encoder-Decoder Networks for Semantic Segmentation. Detection mostly:
See also Convolutional Neural Networks for Object Detection and Segmentation.
See also Semantic Segmentation, Label and Segment Together.
See also Neural Networks for Segmentation.

Wei, Y.C.[Yun-Chao], Liang, X.D.[Xiao-Dan], Chen, Y.P.[Yun-Peng], Jie, Z.[Zequn], 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

Liu, Y.[Yu], Nguyen, D.M.[Duc Minh], Deligiannis, N.[Nikos], Ding, W.[Wenrui], 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

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

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
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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

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
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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

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. BibRef

Zhai, Y.J.[Ying-Jie], Fan, D.P.[Deng-Ping], Yang, J.F.[Ju-Feng], Borji, A.[Ali], Shao, L.[Ling], Han, J.[Junwei], Wang, L.[Liang],
Bifurcated Backbone Strategy for RGB-D Salient Object Detection,
IP(30), 2021, pp. 8727-8742.
IEEE DOI 2111
BibRef
Earlier: A2, A1, A4, A3, A5, Only:
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network,
ECCV20(275-292).
Springer DOI 2010
Feature extraction, Semantics, Decoding, Training, Object detection, Data mining, Image color analysis, cascaded refinement BibRef

Zhang, Z.[Zhao], Lin, Z.[Zheng], Xu, J.[Jun], Jin, W.D.[Wen-Da], Lu, S.P.[Shao-Ping], Fan, D.P.[Deng-Ping],
Bilateral Attention Network for RGB-D Salient Object Detection,
IP(30), 2021, pp. 1949-1961.
IEEE DOI 2101
Feature extraction, Object detection, Image color analysis, Fans, Benchmark testing, Task analysis, Streaming media, RGB-D image BibRef

Jin, W.D.[Wen-Da], Xu, J.[Jun], Han, Q.[Qi], Zhang, Y.[Yi], Cheng, M.M.[Ming-Ming],
CDNet: Complementary Depth Network for RGB-D Salient Object Detection,
IP(30), 2021, pp. 3376-3390.
IEEE DOI 2103
Feature extraction, Ions, Fuses, Task analysis, Object detection, Streaming media, Predictive models, cross-modal feature fusion 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], Lin, L.[Liang], Wang, G.R.[Guang-Run], Wang, M.[Meng], Zuo, W.M.[Wang-Meng],
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions,
PAMI(41), No. 3, March 2019, pp. 596-610.
IEEE DOI 1902
Semantics, Labeling, Training, Neural networks, Task analysis, Predictive models, Image segmentation, Scene parsing, recursive structured prediction BibRef

Lin, L.[Liang], Wang, G.R.[Guang-Run], Zhang, R.[Rui], Zhang, R.M.[Rui-Mao], Liang, X.D.[Xiao-Dan], Zuo, W.M.[Wang-Meng],
Deep Structured Scene Parsing by Learning with Image Descriptions,
CVPR16(2276-2284)
IEEE DOI 1612
BibRef

Zhang, R.[Ruimao], 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

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.[Måns], 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
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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], Lefèvre, S.[Sébastien],
Distance transform regression for spatially-aware deep semantic segmentation,
CVIU(189), 2019, pp. 102809.
Elsevier DOI 1911
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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
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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
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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.[Longzhi],
Semantic combined network for zero-shot scene parsing,
IET-IPR(14), No. 4, 27 March 2020, pp. 757-765.
DOI Link 2003
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Zhang, Y.[Yang], David, P.[Philip], Foroosh, H.[Hassan], Gong, B.[Boqing],
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., Wang, C., Wang, X., Liu, W., Wang, J.,
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.,
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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., Mancini, M., Rota Bulò, S., Ricci, E., Caputo, B.,
Modeling the Background for Incremental Learning in Semantic Segmentation,
CVPR20(9230-9239)
IEEE DOI 2008
Semantics, Image segmentation, Task analysis, Standards, Training, Context modeling, Computer architecture 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],
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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],
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SPLetters(27), 2020, pp. 1640-1644.
IEEE DOI 2010
Semantics, Image segmentation, Neural networks, Visualization, Training, Predictive models, Machine learning, zero-shot 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],
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Wu, T.Y.[Tian-Yi], Tang, S.[Sheng], Zhang, R.[Rui], Cao, J.[Juan], Zhang, Y.D.[Yong-Dong],
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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,
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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,
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Zhang, P.P.[Ping-Ping], Liu, W.[Wei], Zeng, Y.[Yi], Lei, Y.J.[Yin-Jie], Lu, H.C.[Hu-Chuan],
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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
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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

Zhang, Y.[Yu], Sun, X.[Xin], Dong, J.Y.[Jun-Yu], Chen, C.[Changrui], Lv, Q.X.[Qing-Xuan],
GPNet: Gated pyramid network for semantic segmentation,
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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],
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IJCV(129), No. 5, May 2021, pp. 1506-1525.
Springer DOI 2105
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Wang, D.L.[Dong-Li], Li, N.J.[Nan-Jun], Zhou, Y.[Yan], Mu, J.Z.[Jin-Zhen],
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DOI Link 2106
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Yang, W.[Wei], Zhang, J.L.[Jian-Lin], Chen, Z.[Zhongbi], Xu, Z.Y.[Zhi-Yong],
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IET-IPR(15), No. 1, 2021, pp. 57-64.
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Ahmed, I.A.L.[Ifham Abdul Latheef], Jaward, M.H.[Mohamed Hisham],
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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

Grubišic, I.[Ivan], Oršic, M.[Marin], Šegvic, S.[Siniša],
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
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Bao, Y.[Yanqi], Song, K.[Kechen], Wang, J.[Jie], Huang, L.M.[Li-Ming], Dong, H.[Hongwen], Yan, Y.[Yunhui],
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


Zhu, L.[Lanyun], Ji, D.[Deyi], Zhu, S.P.[Shi-Ping], Gan, W.[Weihao], 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

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

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

Lee, J.[Jungbeom], Kim, E.[Eunji], Yoon, S.[Sungroh],
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation,
CVPR21(4070-4078)
IEEE DOI 2111
Location awareness, Image segmentation, Semantics, Pattern recognition, Object recognition 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, Market research, Stability analysis BibRef

Chen, X.K.[Xiao-Kang], Yuan, Y.[Yuhui], 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, Pattern recognition, Standards BibRef

Yao, Y.Z.[Ya-Zhou], Chen, T.[Tao], Xie, G.S.[Guo-Sen], Zhang, C.Y.[Chuan-Yi], Shen, F.[Fumin], 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

Lai, X.[Xin], Tian, Z.[Zhuotao], Jiang, L.[Li], Liu, S.[Shu], Zhao, H.[Hengshuang], Wang, L.[Liwei], 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

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, Pattern recognition, 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.[Meiqing], Liang, M.[Mingfu], 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.[Zhuoying], Wang, Y.[Yongtao], Tang, Z.[Zhi], Li, Y.[Yangyan], 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.[Yajun], Liu, Y.Z.[Ya-Zhou], Sun, Q.[Quansen],
Real-time Semantic Segmentation via Region and Pixel Context Network,
ICPR21(7043-7049)
IEEE DOI 2105
Semantics, Graphics processing units, Real-time systems, Pattern recognition, 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, Pattern recognition, Task analysis BibRef

Shan, L.[Lianlei], Li, M.[Minglong], 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, Pattern recognition, Task analysis, object detection, semantic segmentation. Location awareness, Fuses, Detectors, Feature extraction, Time division multiplexing BibRef

Zhou, H.[Hao], Qi, L.[Lu], Wan, Z.L.[Zhao-Liang], Huang, H.[Hai], Yang, X.[Xu],
RGB-D Co-Attention Network for Semantic Segmentation,
ACCV20(I:519-536).
Springer DOI 2103
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Courdier, E.[Evann], Fleuret, F.[François],
Real-time Segmentation Networks Should be Latency Aware,
ACCV20(I:603-619).
Springer DOI 2103
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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
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Qu, L.Z.[Lin-Zi], He, L.[Lihuo], Ke, J.[Junji], 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
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Peters, T., Brenner, C., Song, M.,
Improving Deep Learning Based Semantic Segmentation with Multi View Outlier Correction,
ISPRS20(B2:711-716).
DOI Link 2012
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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
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Wang, H.C.[Hao-Chen], Zhang, X.D.[Xu-Dong], Hu, Y.[Yutao], 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
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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

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
computer vision, 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, Computer vision 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.[Yajun], Mao, Z.[Zhendong], Zhang, P.[Peng], Wang, B.[Bin],
Compact Position-aware Attention Network for Image Semantic Segmentation,
MMMod20(II:639-650).
Springer DOI 2003
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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

Zhuang, B.[Bohan], Shen, C.H.[Chun-Hua], Tan, M.[Mingkui], Liu, L.Q.[Ling-Qiao], Reid, I.D.[Ian D.],
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation,
CVPR19(413-422).
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. computer vision, 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.[Zhaofan], 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

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

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
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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
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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.[Fumin], 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
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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.[Ruoqi], Zhu, X.G.[Xin-Ge], Wu, C.[Chongruo], 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

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

Tang, M., Djelouah, A., Perazzi, F., Boykov, 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
computer vision, 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

Li, R., Li, K., Kuo, Y., Shu, M., Qi, X., Shen, X., Jia, J.,
Referring Image Segmentation via Recurrent Refinement Networks,
CVPR18(5745-5753)
IEEE DOI 1812
Image segmentation, Semantics, Natural languages, Task analysis, Feature extraction, Logic gates, Training 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, Computer vision 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

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

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

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Convolutional Neural Networks for Semantic Segmentation, CNN .


Last update:Nov 30, 2021 at 22:19:38