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Earlier: A2, A1, A3:
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0806
Constraint satisfaction neural networks (CSNN); Segmentation;
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1604
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DeepProposals: Hunting Objects and Actions by Cascading Deep
Convolutional Layers,
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Springer DOI
1708
BibRef
Earlier:
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers,
ICCV15(2578-2586)
IEEE DOI
1602
Aggregates
BibRef
Nakajima, Y.[Yoshikatsu],
Saito, H.[Hideo],
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Supervised Classification High-Resolution Remote-Sensing Image Based
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RS(10), No. 5, 2018, pp. xx-yy.
DOI Link
1806
BibRef
Wang, C.Y.[Chun-Yan],
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Interval Type-2 Fuzzy Based Neural Network For High Resolution Remote
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ISPRS16(B7: 385-391).
DOI Link
1610
BibRef
Larsson, M.[Mĺns],
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1901
BibRef
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Shi, X.D.[Xiao-Dan],
Shibasaki, R.[Ryosuke],
Shao, X.W.[Xiao-Wei],
A Stacked Fully Convolutional Networks with Feature Alignment
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RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Wang, Q.,
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IEEE DOI
1906
Feature extraction, Object segmentation, Visualization,
Brain modeling, Context modeling, Convolutional neural networks,
conditional Boltzmann machines
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He, C.[Chu],
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An End-to-End Conditional Random Fields and Skip-Connected Generative
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RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Ghosh, S.[Swarnendu],
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1912
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IET-IPR(14), No. 1, January 2020, pp. 183-191.
DOI Link
1912
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Wang, S.[Sherrie],
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Xie, S.M.[Sang Michael],
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Weakly Supervised Deep Learning for Segmentation of Remote Sensing
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RS(12), No. 2, 2020, pp. xx-yy.
DOI Link
2001
BibRef
Yu, J.,
Blaschko, M.B.,
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses,
PAMI(42), No. 3, March 2020, pp. 735-748.
IEEE DOI
2002
Fasteners, Risk management, Optimization, Training,
Complexity theory, Task analysis, Indexes, Lovász extension,
Jaccard index score
BibRef
Li, K.[Kun],
Hu, X.Y.[Xiang-Yun],
Jiang, H.[Huiwei],
Shu, Z.[Zhen],
Zhang, M.[Mi],
Attention-Guided Multi-Scale Segmentation Neural Network for
Interactive Extraction of Region Objects from High-Resolution
Satellite Imagery,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Gu, X.B.[Xian-Bin],
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A multi-feature bipartite graph ensemble for image segmentation,
PRL(131), 2020, pp. 98-104.
Elsevier DOI
2004
Image segmentation, Feature fusion, Bipartite graph, Spectral clustering
BibRef
Guo, Z.K.[Zheng-Kun],
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An adaptive infrared image segmentation method based on fusion SPCNN,
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Elsevier DOI
2007
Infrared image segmentation, Pulse coupled neural network,
Adaptive parameter setting, Output selection
BibRef
Han, L.[Lili],
Li, S.J.[Shu-Juan],
Ren, P.X.[Peng-Xin],
Xue, D.D.[Ding-Dan],
Block cosparsity overcomplete learning transform image segmentation
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IET-IPR(14), No. 10, August 2020, pp. 2074-2080.
DOI Link
2008
BibRef
Ye, L.W.[Lin-Wei],
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Dual Convolutional LSTM Network for Referring Image Segmentation,
MultMed(22), No. 12, December 2020, pp. 3224-3235.
IEEE DOI
2011
Image segmentation, Visualization, Decoding, Linguistics,
Task analysis, Logic gates,
deep learning
BibRef
Ye, L.W.[Lin-Wei],
Rochan, M.[Mrigank],
Liu, Z.[Zhi],
Zhang, X.Q.[Xiao-Qin],
Wang, Y.[Yang],
Referring Segmentation in Images and Videos With Cross-Modal
Self-Attention Network,
PAMI(44), No. 7, July 2022, pp. 3719-3732.
IEEE DOI
2206
BibRef
Earlier: A1, A2, A3, A5, Only:
Cross-Modal Self-Attention Network for Referring Image Segmentation,
CVPR19(10494-10503).
IEEE DOI
2002
Videos, Image segmentation, Visualization, Task analysis,
Linguistics, Feature extraction, Semantics, Referring segmentation,
self-attention
BibRef
Luo, A.[Ao],
Yang, F.[Fan],
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EKENet:
Efficient knowledge enhanced network for real-time scene parsing,
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Elsevier DOI
2012
Scene parsing, Real-time method, Deep learning
BibRef
Li, X.[Xin],
Yang, F.[Fan],
Luo, A.[Ao],
Jiao, Z.C.[Zhi-Cheng],
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Liu, Z.C.[Zi-Cheng],
EFRNet: Efficient Feature Reconstructing Network for Real-Time Scene
Parsing,
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IEEE DOI
2206
Image reconstruction, Semantics, Encoding, Feature extraction,
Convolutional codes, Real-time systems, Task analysis,
representation learning
BibRef
Li, Z.,
Kamnitsas, K.,
Glocker, B.,
Analyzing Overfitting Under Class Imbalance in Neural Networks for
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MedImg(40), No. 3, March 2021, pp. 1065-1077.
IEEE DOI
2103
Image segmentation, Training, Task analysis, Data models,
Training data, Lesions, Sensitivity, Overfitting, class imbalance,
image segmentation
BibRef
Tek, F.B.[F. Boray],
Çam, I.[Ilker],
Karli, D.[Deniz],
Adaptive convolution kernel for artificial neural networks,
JVCIR(75), 2021, pp. 103015.
Elsevier DOI
2103
Adaptive convolution, Multi-scale convolution,
Image classification, Residual networks
See also Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation.
BibRef
Ke, R.H.[Ri-Huan],
Bugeau, A.[Aurélie],
Papadakis, N.[Nicolas],
Kirkland, M.[Mark],
Schuetz, P.[Peter],
Schönlieb, C.B.[Carola-Bibiane],
Multi-Task Deep Learning for Image Segmentation Using Recursive
Approximation Tasks,
IP(30), 2021, pp. 3555-3567.
IEEE DOI
2103
Task analysis, Image segmentation, Annotations, Training, Shape,
Deep learning, Semantics, Image segmentation, multi-task learning,
deep convolutional networks
BibRef
Nalepa, J.[Jakub],
Myller, M.[Michal],
Cwiek, M.[Marcin],
Zak, L.[Lukasz],
Lakota, T.[Tomasz],
Tulczyjew, L.[Lukasz],
Kawulok, M.[Michal],
Towards On-Board Hyperspectral Satellite Image Segmentation:
Understanding Robustness of Deep Learning through Simulating
Acquisition Conditions,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Li, Y.S.[Yan-Sheng],
Shi, T.[Te],
Zhang, Y.J.[Yong-Jun],
Chen, W.[Wei],
Wang, Z.B.[Zhi-Bin],
Li, H.[Hao],
Learning Deep Semantic Segmentation Network Under Multiple
Weakly-Supervised Constraints for Cross-Domain Remote Sensing Image
Semantic Segmentation,
PandRS(175), 2021, pp. 20-33.
Elsevier DOI
2105
Cross-domain remote sensing (RS) image semantic segmentation,
Weakly-supervised transfer invariant constraint (WTIC),
Dynamic optimization strategy
BibRef
Hong, D.F.[Dan-Feng],
Yao, J.[Jing],
Meng, D.Y.[De-Yu],
Xu, Z.B.[Zong-Ben],
Chanussot, J.[Jocelyn],
Multimodal GANs:
Toward Crossmodal Hyperspectral-Multispectral Image Segmentation,
GeoRS(59), No. 6, June 2021, pp. 5103-5113.
IEEE DOI
2106
Image segmentation, Semantics, Robustness, Training,
Feature extraction, Imaging, Classification,
single image training
BibRef
Zhu, W.T.[Wen-Tao],
Myronenko, A.[Andriy],
Xu, Z.Y.[Zi-Yue],
Li, W.Q.[Wen-Qi],
Roth, H.R.[Holger R.],
Huang, Y.F.[Yu-Fang],
Milletari, F.[Fausto],
Xu, D.G.[Da-Guang],
NeurReg: Neural Registration and Its Application to Image
Segmentation,
WACV20(3606-3615)
IEEE DOI
2006
Image segmentation, Training, Strain, Estimation, Task analysis,
Image registration, Neural networks
BibRef
Tian, Z.T.[Zhuo-Tao],
Zhao, H.S.[Heng-Shuang],
Shu, M.[Michelle],
Yang, Z.C.[Zhi-Cheng],
Li, R.Y.[Rui-Yu],
Jia, J.Y.[Jia-Ya],
Prior Guided Feature Enrichment Network for Few-Shot Segmentation,
PAMI(44), No. 2, February 2022, pp. 1050-1065.
IEEE DOI
2201
Semantics, Image segmentation, Object segmentation, Training,
Finite element analysis, Adaptation models, Feature extraction,
scene understanding
BibRef
Zhu, H.M.[Hong-Ming],
Tan, R.[Rui],
Han, L.T.[Le-Tong],
Fan, H.F.[Hong-Fei],
Wang, Z.[Zeju],
Du, B.[Bowen],
Liu, S.C.[Si-Cong],
Liu, Q.[Qin],
DSSM: A Deep Neural Network with Spectrum Separable Module for
Multi-Spectral Remote Sensing Image Segmentation,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhao, Y.C.[Yao-Chi],
Liu, S.G.[Shi-Guang],
Hu, Z.H.[Zhu-Hua],
Focal learning on stranger for imbalanced image segmentation,
IET-IPR(16), No. 5, 2022, pp. 1305-1323.
DOI Link
2203
Training deep net for segmentation with imbalanced data.
BibRef
Khehra, B.S.[Baljit Singh],
Singh, A.[Arjan],
Hura, G.S.[Gurdeep Singh],
Performance evaluation of Shannon and non-Shannon fuzzy 2-partition
entropies for image segmentation using teaching-learning-based
optimisation,
IJCVR(12), No. 3, 2022, pp. 250-298.
DOI Link
2205
BibRef
Beeche, C.[Cameron],
Singh, J.P.[Jatin P.],
Leader, J.K.[Joseph K.],
Gezer, N.S.[Naciye S.],
Oruwari, A.P.[Amechi P.],
Dansingani, K.K.[Kunal K.],
Chhablani, J.[Jay],
Pu, J.T.[Jian-Tao],
Super U-Net: A modularized generalizable architecture,
PR(128), 2022, pp. 108669.
Elsevier DOI
2205
Image segmentation, U-Net, Dynamic receptive field, Fusion upsampling
BibRef
Sun, X.[Xin],
Chen, C.R.[Chang-Rui],
Wang, X.R.[Xiao-Rui],
Dong, J.Y.[Jun-Yu],
Zhou, H.Y.[Hui-Yu],
Chen, S.[Sheng],
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation,
CirSysVideo(32), No. 5, May 2022, pp. 2937-2948.
IEEE DOI
2205
Image segmentation, Convolution, Task analysis, Semantics,
Feature extraction, Training, Kernel, Image segmentation,
dynamic receptive field
BibRef
Zhang, M.[Miao],
Shi, M.J.[Miao-Jing],
Li, L.[Li],
MFNet: Multiclass Few-Shot Segmentation Network with Pixel-Wise
Metric Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8586-8598.
IEEE DOI
2212
Image segmentation, Feature extraction, Task analysis, Semantics,
Prototypes, Measurement, Decoding, Few-shot segmentation,
metric learning
BibRef
Covell, M.[Michele],
Marwood, D.[David],
Baluja, S.[Shumeet],
Adding Non-Linear Context to Deep Networks,
ICIP22(671-675)
IEEE DOI
2211
Training, Deep learning, Convolution, Error analysis,
Neural networks, Object segmentation, Video compression, ResNet
BibRef
Zhang, Y.M.[Yu-Ming],
Hsieh, J.W.[Jun-Wei],
Lee, C.C.[Chun-Chieh],
Fan, K.C.[Kuo-Chin],
SFPN: Synthetic FPN for Object Detection,
ICIP22(1316-1320)
IEEE DOI
2211
Visualization, Fuses, Object detection, Detectors,
Feature extraction, Residual neural networks, object detection, multi-scale
BibRef
Xiong, Z.T.[Zhi-Tong],
Li, H.P.[Hao-Peng],
Zhu, X.X.[Xiao Xiang],
Doubly Deformable Aggregation of Covariance Matrices for Few-Shot
Segmentation,
ECCV22(XX:133-150).
Springer DOI
2211
BibRef
Shi, X.Y.[Xin-Yu],
Wei, D.[Dong],
Zhang, Y.[Yu],
Lu, D.H.[Dong-Huan],
Ning, M.[Munan],
Chen, J.S.[Jia-Shun],
Ma, K.[Kai],
Zheng, Y.F.[Ye-Feng],
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for
Few-Shot Segmentation,
ECCV22(XX:151-168).
Springer DOI
2211
BibRef
He, X.Z.[Xing-Zhe],
Wandt, B.[Bastian],
Rhodin, H.[Helge],
GANSeg: Learning to Segment by Unsupervised Hierarchical Image
Generation,
CVPR22(1215-1225)
IEEE DOI
2210
Training, Image segmentation, Solid modeling, Image synthesis, Shape,
Robustness, Segmentation, grouping and shape analysis,
Self- semi- meta- unsupervised learning
BibRef
Guo, R.[Ruohao],
Niu, D.T.[Dan-Tong],
Qu, L.[Liao],
Li, Z.B.[Zhen-Bo],
SOTR: Segmenting Objects with Transformers,
ICCV21(7137-7146)
IEEE DOI
2203
Training, Tensors, Pipelines, Neural networks, Detectors,
Transformer cores, Transformers, Segmentation, grouping and shape,
Recognition and classification
BibRef
Zhang, B.F.[Bing-Feng],
Xiao, J.[Jimin],
Qin, T.[Terry],
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation,
CVPR21(8308-8317)
IEEE DOI
2111
Support vector machines, Image segmentation,
Codes, Pipelines, Data mining
BibRef
Li, G.[Gen],
Jampani, V.[Varun],
Sevilla-Lara, L.[Laura],
Sun, D.[Deqing],
Kim, J.H.[Jong-Hyun],
Kim, J.[Joongkyu],
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation,
CVPR21(8330-8339)
IEEE DOI
2111
Image segmentation, Adaptation models, Shape,
Computational modeling, Prototypes, Feature extraction
BibRef
Boudiaf, M.[Malik],
Kervadec, H.[Hoel],
Masud, Z.I.[Ziko Imtiaz],
Piantanida, P.[Pablo],
Ben Ayed, I.[Ismail],
Dolz, J.[Jose],
Few-Shot Segmentation Without Meta-Learning:
A Good Transductive Inference Is All You Need?,
CVPR21(13974-13983)
IEEE DOI
2111
Training, Image segmentation, Codes,
Benchmark testing, Feature extraction, Entropy
BibRef
Pham, D.D.[Duc Duy],
Dovletov, G.[Gurbandurdy],
Pauli, J.[Josef],
A Differentiable Convolutional Distance Transform Layer for Improved
Image Segmentation,
GCPR20(432-444).
Springer DOI
2110
BibRef
Burrows, L.[Liam],
Chen, K.[Ke],
Torella, F.[Francesco],
A Deep Image Prior Learning Algorithm for Joint Selective Segmentation
and Registration,
SSVM21(411-422).
Springer DOI
2106
BibRef
Raipuria, G.[Geetank],
Bonthu, S.[Saikiran],
Singhal, N.[Nitin],
Noise Robust Training of Segmentation Model Using Knowledge
Distillation,
AIDP20(97-104).
Springer DOI
2103
BibRef
Wang, H.C.,
General Deep Learning Segmentation Process Used In Remote Sensing
Images,
ISPRS20(B2:1289-1296).
DOI Link
2012
BibRef
Li, X.,
Liu, Y.,
Xu, K.,
Zhao, Z.,
Liu, S.,
A Context-Based Network For Referring Image Segmentation,
ICIP20(1436-1440)
IEEE DOI
2011
Image segmentation, Visualization, Linguistics, Feature extraction,
Convolution, Decoding, Referring Image Segmentation,
Dense Convolution
BibRef
Beheshti, N.,
Johnsson, L.,
Squeeze U-Net: A Memory and Energy Efficient Image Segmentation
Network,
WiCV20(1495-1504)
IEEE DOI
2008
Fires, Computational modeling, Kernel, Feature extraction,
Graphics processing units, Memory management
BibRef
Kundu, J.N.,
Rajput, G.S.[G. Singh],
Babu, R.V.,
VRT-Net: Real-Time Scene Parsing via Variable Resolution Transform,
WACV20(2038-2045)
IEEE DOI
2006
Image segmentation, Transforms, Estimation, Real-time systems,
Spatial resolution, Computer architecture
BibRef
Kim, Y.,
Choi, S.,
Lee, H.,
Kim, T.,
Kim, C.,
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised
Video Object Segmentation,
WACV20(2046-2054)
IEEE DOI
2006
Convolution, Training, Object segmentation, Feature extraction,
Robustness, Image segmentation, Image color analysis
BibRef
Park, H.,
Sjösund, L.L.,
Yoo, Y.,
Monet, N.,
Bang, J.,
Kwak, N.,
SINet: Extreme Lightweight Portrait Segmentation Networks with
Spatial Squeeze Modules and Information Blocking Decoder,
WACV20(2055-2063)
IEEE DOI
2006
Image segmentation, Decoding, Convolution, Task analysis,
Feature extraction, Uncertainty, Computational modeling
BibRef
Wang, W.,
Yu, K.,
Hugonot, J.,
Fua, P.,
Salzmann, M.,
Recurrent U-Net for Resource-Constrained Segmentation,
ICCV19(2142-2151)
IEEE DOI
2004
image segmentation, recurrent neural nets, segmentation methods,
deep networks, standard GPUs, recurrent U-Net architecture, Tensile stress
BibRef
Ding, H.,
Jiang, X.,
Liu, A.Q.,
Magnenat-Thalmann, N.[Nadia],
Wang, G.,
Boundary-Aware Feature Propagation for Scene Segmentation,
ICCV19(6818-6828)
IEEE DOI
2004
feature extraction, graph theory, image segmentation,
learning (artificial intelligence), segment regions,
Convolution
BibRef
Cao, J.[Jiale],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Triply Supervised Decoder Networks for Joint Detection and Segmentation,
CVPR19(7384-7393).
IEEE DOI
2002
BibRef
Pi, P.C.[Peng-Cheng],
Jiang, Z.[Ziyu],
Xiong, Z.X.[Zi-Xiang],
Learning Skip Map for Efficient Ultra-High Resolution Image
Segmentation,
ICIP21(634-638)
IEEE DOI
2201
Image segmentation, Image resolution, Computational complexity,
Image segmentation, ultra-high resolution images, skip map
BibRef
Chen, W.Y.[Wu-Yang],
Jiang, Z.[Ziyu],
Wang, Z.Y.[Zhang-Yang],
Cui, K.[Kexin],
Qian, X.N.[Xiao-Ning],
Collaborative Global-Local Networks for Memory-Efficient Segmentation
of Ultra-High Resolution Images,
CVPR19(8916-8925).
IEEE DOI
2002
BibRef
Durall, R.[Ricard],
Pfreundt, F.J.[Franz-Josef],
Köthe, U.[Ullrich],
Keuper, J.[Janis],
Object Segmentation Using Pixel-Wise Adversarial Loss,
GCPR19(303-316).
Springer DOI
1911
BibRef
Hu, T.[Tao],
Dense In Dense: Training Segmentation from Scratch,
ACCV18(VI:454-470).
Springer DOI
1906
BibRef
Pandey, G.,
Dukkipati, A.,
Learning to Segment With Image-Level Supervision,
WACV19(1856-1865)
IEEE DOI
1904
convolution, image classification, image representation,
image segmentation, learning (artificial intelligence),
Force
BibRef
Marin, D.[Dmitrii],
Tang, M.[Meng],
Ben Ayed, I.[Ismail],
Boykov, Y.Y.[Yuri Y.],
Beyond Gradient Descent for Regularized Segmentation Losses,
CVPR19(10179-10188).
IEEE DOI
2002
BibRef
Tang, M.[Meng],
Perazzi, F.[Federico],
Djelouah, A.[Abdelaziz],
Ben Ayed, I.[Ismail],
Schroers, C.[Christopher],
Boykov, Y.Y.[Yuri Y.],
On Regularized Losses for Weakly-supervised CNN Segmentation,
ECCV18(XVI: 524-540).
Springer DOI
1810
BibRef
Oudni, L.,
Vázquez, C.,
Coulombe, S.,
Motion Occlusions for Automatic Generation of Relative Depth Maps,
ICIP18(1538-1542)
IEEE DOI
1809
Optical imaging, Integrated optics, Image color analysis,
Estimation, Coherence, Interpolation, Image segmentation,
BibRef
Morley, D.,
Foroosh, H.,
Improving RANSAC-Based Segmentation through CNN Encapsulation,
CVPR17(2661-2670)
IEEE DOI
1711
Encapsulation, Feature extraction,
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IEEE DOI
1711
Biological system modeling, Computational modeling, Convolution,
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ICCV15(1796-1804)
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Convolutional codes
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Learning shape priors for object segmentation via neural networks,
ICIP15(1835-1839)
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1512
Object segmentation; convolutional neural networks; shape priors
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Neural Networks for Semantic Segmentation .