8.6.3 Semantic Segmentation, Label and Segment Together

Csurka, G.[Gabriela], Perronnin, F.[Florent],
An Efficient Approach to Semantic Segmentation,
IJCV(95), No. 2, November 2011, pp. 198-212.
WWW Link. 1109
BibRef
Earlier:
A Simple High Performance Approach to Semantic Segmentation,
BMVC08(xx-yy).
PDF File. 0809
Assign each pixel to a semantic class. A local appearance model, a local consistency model and a global consistency model.
See also Universal and Adapted Vocabularies for Generic Visual Categorization. BibRef

Perronnin, F.[Florent], Dance, C.R.[Christopher R.],
Fisher Kernels on Visual Vocabularies for Image Categorization,
CVPR07(1-8).
IEEE DOI 0706
BibRef
Earlier: Csurka, G.[Gabriela], Bressan, M.[Marco],
Adapted Vocabularies for Generic Visual Categorization,
ECCV06(IV: 464-475).
Springer DOI 0608
BibRef

Csurka, G.[Gabriela], Dance, C.R.[Christopher R.], Perronnin, F.[Florent], Willamowski, J.[Jutta],
Generic Visual Categorization Using Weak Geometry,
CLOR06(207-224).
Springer DOI 0711
BibRef
Earlier: A1, A4, A2, A3:
Incorporating Geometry Information with Weak Classifiers for Improved Generic Visual Categorization,
CIAP05(612-620).
Springer DOI 0509
BibRef

Kemmler, M.[Michael], Rodner, E.[Erik], Wacker, E.S.[Esther-Sabrina], Denzler, J.[Joachim],
One-Class Classification with Gaussian Processes,
PR(46), No. 12, 2013, pp. 3507-3518.
Elsevier DOI 1307
BibRef
Earlier: A1, A2, A4, Only: ACCV10(II: 489-500).
Springer DOI 1011
BibRef
Earlier: A1, A2, A4, Only:
Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features,
Dyn3D09(96-109).
Springer DOI 0909
Combine range and visual, determine general class of scene. One-class classification BibRef

Bodesheim, P.[Paul], Freytag, A.[Alexander], Rodner, E.[Erik], Denzler, J.[Joachim],
Approximations of Gaussian Process Uncertainties for Visual Recognition Problems,
SCIA13(182-194).
Springer DOI 1311
BibRef
Earlier: A1, A3, A2, A4:
Divergence-Based One-Class Classification Using Gaussian Processes,
BMVC12(50).
DOI Link 1301

See also Classification of Microorganisms via Raman Spectroscopy Using Gaussian Processes. BibRef

Käding, C.[Christoph], Freytag, A.[Alexander], Rodner, E.[Erik], Perino, A.[Andrea], Denzler, J.[Joachim],
Large-Scale Active Learning with Approximations of Expected Model Output Changes,
GCPR16(179-191).
Springer DOI 1611
BibRef

Freytag, A.[Alexander], Rodner, E.[Erik], Denzler, J.[Joachim],
Selecting Influential Examples: Active Learning with Expected Model Output Changes,
ECCV14(IV: 562-577).
Springer DOI 1408
BibRef

Rodner, E.[Erik], Freytag, A.[Alexander], Bodesheim, P.[Paul], Fröhlich, B.[Björn], Denzler, J.[Joachim],
Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks,
IJCV(121), No. 2, January 2017, pp. 253-280.
Springer DOI 1702
BibRef

Freytag, A.[Alexander], Rodner, E.[Erik], Bodesheim, P.[Paul], Denzler, J.[Joachim],
Labeling Examples That Matter: Relevance-Based Active Learning with Gaussian Processes,
GCPR13(282-291).
Springer DOI 1311
BibRef
Earlier:
Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels,
ACCV12(II:511-524).
Springer DOI 1304
BibRef

Freytag, A.[Alexander], Frohlich, B.[Bjorn], Rodner, E.[Erik], Denzler, J.[Joachim],
Efficient semantic segmentation with Gaussian processes and histogram intersection kernels,
ICPR12(3313-3316).
WWW Link. 1302
BibRef

Rodner, E.[Erik], Freytag, A.[Alexander], Bodesheim, P.[Paul], Denzler, J.[Joachim],
Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels,
ECCV12(IV: 85-98).
Springer DOI 1210
BibRef

Rodner, E.[Erik], Hegazy, D., Denzler, J.[Joachim],
Multiple kernel Gaussian process classification for generic 3D object recognition,
IVCNZ10(1-8).
IEEE DOI 1203
BibRef

Pei, D.L.[De-Li], Li, Z.G.[Zhen-Guo], Ji, R.R.[Rong-Rong], Sun, F.C.[Fu-Chun],
Efficient semantic image segmentation with multi-class ranking prior,
CVIU(120), No. 1, 2014, pp. 81-90.
Elsevier DOI 1403
Computer vision BibRef

Osuna-Enciso, V.[Valentín],
Bioinspired metaheuristics for image segmentation,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link 1407
Ph.D.. Thesis. BibRef

Wang, L.[Le], Hua, G.[Gang], Xue, J.R.[Jian-Ru], Gao, Z.N.[Zhan-Ning], Zheng, N.N.[Nan-Ning],
Joint Segmentation and Recognition of Categorized Objects From Noisy Web Image Collection,
IP(23), No. 9, September 2014, pp. 4070-4086.
IEEE DOI 1410
image retrieval BibRef

Gould, S.[Stephen], He, X.M.[Xu-Ming],
Scene Understanding by Labeling Pixels,
CACM(57), No. 11, November 2014, pp. 68-77.
DOI Link 1411
Pixels labeled with a scene's semantics and geometry let computers describe what they see. BibRef

Liu, Z.[Zhi], Xu, S.Q.[Shu-Qiong], Zhang, Y.[Yun], Chen, C.L.P.,
A Multiple-Feature and Multiple-Kernel Scene Segmentation Algorithm for Humanoid Robot,
Cyber(44), No. 11, November 2014, pp. 2232-2241.
IEEE DOI 1411
Gabor filters BibRef

Kang, H., Hebert, M., Efros, A.A., Kanade, T.,
Data-Driven Objectness,
PAMI(37), No. 1, January 2015, pp. 189-195.
IEEE DOI 1412
Databases. Is it an object, not a specific object. BibRef

Wang, L.L.[Li-Li], Yung, N.H.C.[Nelson H.C.],
Hybrid graphical model for semantic image segmentation,
JVCIR(28), No. 1, 2015, pp. 83-96.
Elsevier DOI 1503
Semantic segmentation BibRef

Wang, L.L.[Li-Li], Yung, N.H.C.[Nelson H. C.],
Improved hierarchical conditional random field model for object segmentation,
MVA(26), No. 7-8, November 2015, pp. 1027-1043.
WWW Link. 1511
BibRef

Xia, W.[Wei], Domokos, C.[Csaba], Cheong, L.F.[Loong-Fah], Yan, S.C.[Shui-Cheng],
Background Context Augmented Hypothesis Graph for Object Segmentation,
CirSysVideo(25), No. 4, April 2015, pp. 582-594.
IEEE DOI 1504
Context BibRef

Xia, W.[Wei], Domokos, C.[Csaba], Xiong, J., Cheong, L.F.[Loong-Fah], Yan, S.C.[Shui-Cheng],
Segmentation Over Detection via Optimal Sparse Reconstructions,
CirSysVideo(25), No. 8, August 2015, pp. 1295-1308.
IEEE DOI 1508
Bismuth BibRef

Xia, W.[Wei], Domokos, C.[Csaba], Dong, J.[Jian], Cheong, L.F.[Loong-Fah], Yan, S.C.[Shui-Cheng],
Semantic Segmentation without Annotating Segments,
ICCV13(2176-2183)
IEEE DOI 1403
Given object bounding box. BibRef

Kumar, M.P., Turki, H., Preston, D., Koller, D.,
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation,
PAMI(37), No. 7, July 2015, pp. 1373-1386.
IEEE DOI 1506
Biological system modeling BibRef

Mottaghi, R.[Roozbeh], Fidler, S.[Sanja], Yuille, A.L., Urtasun, R.[Raquel], Parikh, D.[Devi],
Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding,
PAMI(38), No. 1, January 2016, pp. 74-87.
IEEE DOI 1601
Analytical models BibRef

Mottaghi, R.[Roozbeh], Fidler, S.[Sanja], Yao, J.[Jian], Urtasun, R.[Raquel], Parikh, D.[Devi],
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs,
CVPR13(3143-3150)
IEEE DOI 1309
Explore effects by using humans in loop for parts of the problem. BibRef

Ravì, D., Bober, M., Farinella, G.M., Guarnera, M., Battiato, S.,
Semantic segmentation of images exploiting DCT based features and random forest,
PR(52), No. 1, 2016, pp. 260-273.
Elsevier DOI 1601
Semantic segmentation BibRef

Tung, F.[Frederick], Little, J.J.[James J.],
Scene parsing by nonparametric label transfer of content-adaptive windows,
CVIU(143), No. 1, 2016, pp. 191-200.
Elsevier DOI 1601
BibRef
Earlier:
CollageParsing: Nonparametric Scene Parsing by Adaptive Overlapping Windows,
ECCV14(VI: 511-525).
Springer DOI 1408
Image parsing BibRef

Zarchi, M.S., Tan, R.T., van Gemeren, C.[Coert], Monadjemi, A., Veltkamp, R.C.[Remco C.],
Understanding image concepts using ISTOP model,
PR(53), No. 1, 2016, pp. 174-183.
Elsevier DOI 1602
Visual term. Image parsing. BibRef

Diebold, J.[Julia], Nieuwenhuis, C.[Claudia], Cremers, D.[Daniel],
Midrange Geometric Interactions for Semantic Segmentation,
IJCV(117), No. 3, May 2016, pp. 199-225.
Springer DOI 1605

See also Convex Optimization for Scene Understanding.
See also Proximity Priors for Variational Semantic Segmentation and Recognition. BibRef

Nieuwenhuis, C.[Claudia], Strekalovskiy, E.[Evgeny], Cremers, D.[Daniel],
Proportion Priors for Image Sequence Segmentation,
ICCV13(2328-2335)
IEEE DOI 1403
BibRef
Earlier: A2, A1, A3:
Nonmetric Priors for Continuous Multilabel Optimization,
ECCV12(VII: 208-221).
Springer DOI 1210
BibRef

Hazirbas, C.[Caner], Diebold, J.[Julia], Cremers, D.[Daniel],
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation,
SSVM15(243-255).
Springer DOI 1506
BibRef

Zand, M., Doraisamy, S., Halin, A.A.[A. Abdul], Mustaffa, M.R.,
Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs,
IP(25), No. 7, July 2016, pp. 3233-3248.
IEEE DOI 1606
image segmentation BibRef

Pont-Tuset, J.[Jordi], Arbeláez, P.[Pablo], Barron, J.T.[Jon T.], Marques, F.[Ferran], Malik, J.[Jitendra],
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation,
PAMI(39), No. 1, January 2017, pp. 128-140.
IEEE DOI 1612
BibRef
Earlier: A2, A1, A3, A4, A5:
Multiscale Combinatorial Grouping,
CVPR14(328-335)
IEEE DOI 1409
Detectors. Image Segmentation; Object Candidates BibRef

Hu, R., Dollár, P., He, K., Darrell, T.J., Girshick, R.[Ross],
Learning to Segment Every Thing,
CVPR18(4233-4241)
IEEE DOI 1812
Training, Task analysis, Visualization, Transfer functions, Predictive models, Image segmentation, Genomics BibRef

Hariharan, B.[Bharath], Arbeláez, P.[Pablo], Girshick, R.[Ross], Malik, J.[Jitendra],
Simultaneous Detection and Segmentation,
ECCV14(VII: 297-312).
Springer DOI 1408

See also Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation. BibRef

Arbelaez, P.[Pablo], Hariharan, B.[Bharath], Gu, C.H.[Chun-Hui], Gupta, S.[Saurabh], Bourdev, L.[Lubomir], Malik, J.[Jitendra],
Semantic segmentation using regions and parts,
CVPR12(3378-3385).
IEEE DOI 1208
BibRef

Czuni, L.[Laszlo], Kiss, P.J.[Peter Jozsef], Lipovits, A.[Agnes], Gal, M.[Monika],
Lightweight mobile object recognition,
ICIP14(3426-3428)
IEEE DOI 1502
Cameras CEDD (Color and Edge Directivity Descriptor). BibRef

Marmanis, D., Schindler, K., Wegner, J.D., Galliani, S., Datcu, M., Stilla, U.,
Classification with an edge: Improving semantic image segmentation with boundary detection,
PandRS(135), No. Supplement C, 2018, pp. 158-172.
Elsevier DOI 1712
BibRef

Zhang, J.[Jing], Mu, Y.K.[Ya-Kun], Feng, S.W.[Sheng-Wei], Li, K.H.[Ke-Huang], Yuan, Y.[Yubo], Lee, C.H.[Chin-Hui],
Image region annotation based on segmentation and semantic correlation analysis,
IET-IPR(12), No. 8, August 2018, pp. 1331-1337.
DOI Link 1808
BibRef

Shi, H., Li, H., Meng, F., Wu, Q., Xu, L., Ngan, K.N.,
Hierarchical Parsing Net: Semantic Scene Parsing From Global Scene to Objects,
MultMed(20), No. 10, October 2018, pp. 2670-2682.
IEEE DOI 1810
backpropagation, feature extraction, image classification, image coding, local appearance features, contextual feature, context learning BibRef

Noormohamadi, N.[Neda], Adibi, P.[Peyman], Ehsani, S.M.S.[Sayyed Mohammad Saeed],
Semantic image segmentation using an improved hierarchical graphical model,
IET-IPR(12), No. 11, November 2018, pp. 1943-1950.
DOI Link 1810
BibRef

Ates, H.F.[Hasan F.], Sunetci, S.[Sercan],
Multi-hypothesis contextual modeling for semantic segmentation,
PRL(117), 2019, pp. 104-110.
Elsevier DOI 1901
Image parsing, Segmentation, Superpixel, MRF BibRef

Zhou, H.[Hao], Han, A.[Anqi], Yang, H.D.[Hao-Dong], Zhang, J.[Jun],
Edge gradient feature and long distance dependency for image semantic segmentation,
IET-CV(13), No. 1, February 2019, pp. 53-60.
DOI Link 1902
BibRef

Joy, T.[Thomas], Desmaison, A.[Alban], Ajanthan, T.[Thalaiyasingam], Bunel, R.[Rudy], Salzmann, M.[Mathieu], Kohli, P.[Pushmeet], Torr, P.H.S.[Philip H. S.], Kumar, M.P.[M. Pawan],
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials,
SIIMS(12), No. 1, 2019, pp. 287-318.
DOI Link 1904
Segmentation and stereo matching. BibRef

Blott, G.[Gregor], Takami, M.[Masato], Heipke, C.[Christian],
Semantic Segmentation of Fisheye Images,
CVRoads18(I:181-196).
Springer DOI 1905
BibRef

Li, X., Ma, H., Luo, X.,
Weaklier Supervised Semantic Segmentation With Only One Image Level Annotation per Category,
IP(29), No. 1, 2020, pp. 128-141.
IEEE DOI 1910
image recognition, image segmentation, object recognition, supervised learning, weaklier supervised semantic segmentation, dual-branch iterative learning BibRef

Song, Y., Ou, Z.,
Semi-Supervised Seq2seq Joint-Stochastic-Approximation Autoencoders With Applications to Semantic Parsing,
SPLetters(27), 2020, pp. 31-35.
IEEE DOI 2001
Semi-supervised learning, seq2seq, semantic parsing, joint stochastic approximation, variational auto-encoder BibRef

Yang, Z.E.[Zheng-Eng], Yu, H.S.[Hong-Shan], Feng, M.T.[Ming-Tao], Sun, W.[Wei], Lin, X.F.[Xue-Fei], Sun, M.G.[Min-Gui], Mao, Z.H.[Zhi-Hong], Mian, A.[Ajmal],
Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation,
IP(29), 2020, pp. 5175-5190.
IEEE DOI 2004
For driving application. Convolution, Image segmentation, Semantics, Real-time systems, Standards, Training, Computational modeling, Semantic segmentation, synthetic dataset BibRef

Valada, A.[Abhinav], Mohan, R.[Rohit], Burgard, W.[Wolfram],
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation,
IJCV(128), No. 5, May 2020, pp. 1239-1285.
Springer DOI 2005
BibRef

Mohan, R.[Rohit], Valada, A.[Abhinav],
EfficientPS: Efficient Panoptic Segmentation,
IJCV(129), No. 5, May 2021, pp. 1551-1579.
Springer DOI 2105
BibRef

El Houfi, S.[Safae], Majda, A.[Aicha],
Efficient use of recent progresses for Real-time Semantic segmentation,
MVA(31), No. 6, August 2020, pp. Article45.
WWW Link. 2008
BibRef

Chen, Y.[Yifu], Dapogny, A.[Arnaud], Cord, M.[Matthieu],
SEMEDA: Enhancing segmentation precision with semantic edge aware loss,
PR(108), 2020, pp. 107557.
Elsevier DOI 2008
Semantic segmentation, Loss function BibRef

Arunkumar, M.[Manonmani], Pushparaj, V.[Vijayakumari],
Seed picking crossover optimisation algorithm for semantic segmentation from images,
IET-IPR(14), No. 11, September 2020, pp. 2503-2511.
DOI Link 2009
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And: Erratum (adds the authors): IET-IPR(15), No. 13, November, pp. 3410-3410.
DOI Link 2110
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Oršic, M.[Marin], Šegvic, S.[Siniša],
Efficient semantic segmentation with pyramidal fusion,
PR(110), 2021, pp. 107611.
Elsevier DOI 2011
Semantic segmentation, Real-time inference, Shared resolution pyramid, Deep learning BibRef

Lian, X.[Xuhang], Pang, Y.W.[Yan-Wei], Han, J.G.[Jun-Gong], Pan, J.[Jing],
Cascaded hierarchical atrous spatial pyramid pooling module for semantic segmentation,
PR(110), 2021, pp. 107622.
Elsevier DOI 2011
Semantic segmentation, Atrous convolution, Atrous spatial pyramid pooling(ASPP), Cascaded module BibRef

Pemasiri, A.[Akila], Nguyen, K.[Kien], Sridharan, S.[Sridha], Fookes, C.[Clinton],
Multi-modal semantic image segmentation,
CVIU(202), 2021, pp. 103085.
Elsevier DOI 2012
Segmentation, X-ray, Mask R-CNN, Neural networks BibRef

Choi, H.[Hyunguk], Ahn, H.[Hoyeon], Kim, J.[Joonmo], Jeon, M.[Moongu],
ADFNet: Accumulated decoder features for real-time semantic segmentation,
IET-CV(14), No. 8, December 2020, pp. 555-563.
DOI Link 2012
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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

Tasar, O., Giros, A., Tarabalka, Y., Alliez, P., Clerc, S.,
DAugNet: Unsupervised, Multisource, Multitarget, and Life-Long Domain Adaptation for Semantic Segmentation of Satellite Images,
GeoRS(59), No. 2, February 2021, pp. 1067-1081.
IEEE DOI 2101
Satellites, Training, Semantics, Image segmentation, Adaptation models, Remote sensing, Standardization, semantic segmentation BibRef

Zhou, W., Wang, Y., Chu, J., Yang, J., Bai, X., Xu, Y.,
Affinity Space Adaptation for Semantic Segmentation Across Domains,
IP(30), 2021, pp. 2549-2561.
IEEE DOI 2102
Semantics, Image segmentation, Cleaning, Annotations, Adaptation models, Data models, Training, affinity relationship BibRef

Huang, L.[Li], He, M.L.[Mei-Ling], Tan, C.[Chong], Jiang, D.[Du], Li, G.F.[Gong-Fa], Yu, H.[Hui],
Special Issue Retraction: Jointly network image processing: multi-task image semantic segmentation of indoor scene based on CNN,
IET-IPR(17), No. 1, January 2023, pp. 301.
DOI Link 2301
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And: IET-IPR(14), No. 15, 15 December 2020, pp. 3689-3697.
DOI Link 2103
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Zheng, Z.D.[Zhe-Dong], Yang, Y.[Yi],
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation,
IJCV(129), No. 4, April 2021, pp. 1106-1120.
Springer DOI 2104
BibRef

Ji, J.[Jian], Shi, R.[Rui], Li, S.T.[Si-Tong], Chen, P.[Peng], Miao, Q.G.[Qi-Guang],
Encoder-Decoder With Cascaded CRFs for Semantic Segmentation,
CirSysVideo(31), No. 5, 2021, pp. 1926-1938.
IEEE DOI 2105
BibRef

Yan, J.B.[Jie-Bin], Zhong, Y.[Yu], Fang, Y.M.[Yu-Ming], Wang, Z.Y.[Zhang-Yang], Ma, K.D.[Ke-De],
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition,
IJCV(129), No. 5, May 2021, pp. 1768-1786.
Springer DOI 2105
BibRef

Feng, Y.C.[Ying-Chao], Sun, X.[Xian], Diao, W.H.[Wen-Hui], Li, J.[Jihao], Gao, X.[Xin],
Double Similarity Distillation for Semantic Image Segmentation,
IP(30), 2021, pp. 5363-5376.
IEEE DOI 2106
Knowledge engineering, Image segmentation, Correlation, Semantics, Task analysis, Complexity theory, Training, convolutional neural networks BibRef

Weng, X.[Xi], Yan, Y.[Yan], Chen, S.[Si], Xue, J.H.[Jing-Hao], Wang, H.Z.[Han-Zi],
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes,
CirSysVideo(32), No. 7, July 2022, pp. 4444-4459.
IEEE DOI 2207
Semantics, Decoding, Real-time systems, Image segmentation, Training, Aggregates, Predictive models, Real-time semantic segmentation, feature alignment and aggregation BibRef

Dong, G.S.[Gen-Shun], Yan, Y.[Yan], Shen, C.H.[Chun-Hua], Wang, H.Z.[Han-Zi],
Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes,
ITS(22), No. 6, June 2021, pp. 3258-3274.
IEEE DOI 2106
Semantics, Real-time systems, Image segmentation, Convolution, Intelligent transportation systems, Task analysis, light-weight convolutional neural networks BibRef

Weng, X.[Xi], Yan, Y.[Yan], Dong, G.S.[Gen-Shun], Shu, C.[Chang], Wang, B.[Biao], Wang, H.Z.[Han-Zi], Zhang, J.[Ji],
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes,
ITS(23), No. 10, October 2022, pp. 17224-17240.
IEEE DOI 2210
Semantics, Real-time systems, Image segmentation, Lattices, Decoding, Task analysis, Feature extraction, Deep learning, multi-branch aggregation BibRef

Liu, M.Y.[Meng-Yu], Yin, H.J.[Hu-Jun],
Efficient pyramid context encoding and feature embedding for semantic segmentation,
IVC(111), 2021, pp. 104195.
Elsevier DOI 2106
Semantic segmentation, Convolutional neural networks, Pyramid context encoding, Real-time processing BibRef

Jin, Y.[Youngsaeng], Han, D.[David], Ko, H.S.[Han-Seok],
TrSeg: Transformer for semantic segmentation,
PRL(148), 2021, pp. 29-35.
Elsevier DOI 2107
Semantic segmentation, Scene understanding, Transformer, Multi-scale contextual information BibRef

Hu, J.[Jie], Kong, H.F.[Hui-Fang], Fan, L.[Lei], Zhou, J.[Jun],
Enhancing feature fusion with spatial aggregation and channel fusion for semantic segmentation,
IET-CV(15), No. 6, 2021, pp. 418-427.
DOI Link 2108
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Li, X.T.[Xiang-Tai], Li, X.[Xia], You, A.S.[An-Sheng], Zhang, L.[Li], Cheng, G.L.[Guang-Liang], Yang, K.Y.[Kui-Yuan], Tong, Y.H.[Yun-Hai], Lin, Z.C.[Zhou-Chen],
Towards Efficient Scene Understanding via Squeeze Reasoning,
IP(30), 2021, pp. 7050-7063.
IEEE DOI 2108
Semantics, Cognition, Image segmentation, Task analysis, Computational modeling, Convolution, Context modeling, scene understanding BibRef

Li, X.T.[Xiang-Tai], Li, X.[Xia], Zhang, L.[Li], Cheng, G.L.[Guang-Liang], Shi, J.P.[Jian-Ping], Lin, Z.C.[Zhou-Chen], Tan, S.H.[Shao-Hua], Tong, Y.H.[Yun-Hai],
Improving Semantic Segmentation via Decoupled Body and Edge Supervision,
ECCV20(XVII:435-452).
Springer DOI 2011
BibRef

Hao, X.C.[Xiao-Chen], Hao, X.J.[Xing-Jun], Zhang, Y.[Yaru], Li, Y.Y.[Yuan-Yuan], Wu, C.[Chao],
Real-time semantic segmentation with weighted factorized-depthwise convolution,
IVC(114), 2021, pp. 104269.
Elsevier DOI 2109
Semantic segmentation, Real-time, Pyramid fusion, Continuous separation BibRef

Xiao, C.J.[Cun-Jun], Hao, X.J.[Xing-Jun], Li, H.B.[Hai-Bin], Li, Y.Q.[Ya-Qian], Zhang, W.M.[Wen-Ming],
Real-time semantic segmentation with local spatial pixel adjustment,
IVC(123), 2022, pp. 104470.
Elsevier DOI 2206
Semantic segmentation, Real-time, Local spatial adjustment, Dual-branch decoding BibRef

Tang, Q.[Quan], Liu, F.G.[Fa-Gui], Zhang, T.[Tong], Jiang, J.[Jun], Zhang, Y.[Yu],
Attention-guided chained context aggregation for semantic segmentation,
IVC(115), 2021, pp. 104309.
Elsevier DOI 2110
Semantic segmentation, Multi-scale contexts, Series-parallel hybrid streams, Convolutional networks BibRef

Cai, L.[Lile], Xu, X.[Xun], Zhang, L.N.[Li-Ning], Foo, C.S.[Chuan-Sheng],
Exploring Spatial Diversity for Region-Based Active Learning,
IP(30), 2021, pp. 8702-8712.
IEEE DOI 2110
Uncertainty, Spatial diversity, Semantics, Image segmentation, Task analysis, Labeling, Annotations, Active learning, semantic segmentation BibRef

Zhang, J.[Jian], Qi, L.[Lei], Shi, Y.H.[Ying-Huan], Gao, Y.[Yang],
Generalizable model-agnostic semantic segmentation via target-specific normalization,
PR(122), 2022, pp. 108292.
Elsevier DOI 2112
Domain generalization, Semantic segmentation, Model-agnostic learning, Target-specific normalization BibRef

Yu, L.T.[Li-Tao], Li, Z.B.[Zhi-Bin], Xu, M.[Min], Gao, Y.S.[Yong-Sheng], Luo, J.B.[Jie-Bo], Zhang, J.[Jian],
Distribution-Aware Margin Calibration for Semantic Segmentation in Images,
IJCV(130), No. 1, January 2022, pp. 95-110.
Springer DOI 2201
BibRef

Zhou, Q.[Quan], Wu, X.F.[Xiao-Fu], Zhang, S.F.[Suo-Fei], Kang, B.[Bin], Ge, Z.Y.[Zong-Yuan], Latecki, L.J.[Longin Jan],
Contextual ensemble network for semantic segmentation,
PR(122), 2022, pp. 108290.
Elsevier DOI 2112
Ensemble deconvolution, Semantic segmentation, FCNs, Context aggregation, Encoder-decoder networks BibRef

Liu, X.F.[Xiao-Feng], Lu, Y.H.[Yun-Hong], Liu, X.C.[Xiong-Chang], Bai, S.[Song], Li, S.[Site], You, J.[Jane],
Wasserstein Loss With Alternative Reinforcement Learning for Severity-Aware Semantic Segmentation,
ITS(23), No. 1, January 2022, pp. 587-596.
IEEE DOI 2201
Automobiles, Measurement, Roads, Semantics, Optimization, Training, Histograms, Semantic segmentation, autonomous driving, actor-critic BibRef

Liu, X.F.[Xiao-Feng], Ji, W.X.[Wen-Xuan], You, J.[Jane], El Fakhri, G.[Georges], Woo, J.H.[Jong-Hye],
Severity-Aware Semantic Segmentation With Reinforced Wasserstein Training,
CVPR20(12563-12572)
IEEE DOI 2008
Semantics, Autonomous vehicles, Measurement, Automobiles, Histograms, Training, Roads BibRef

Ma, T.Q.[Tian-Qi], Wang, Q.L.[Qi-Long], Zhang, H.Z.[Hong-Zhi], Zuo, W.M.[Wang-Meng],
Delving Deeper Into Pixel Prior for Box-Supervised Semantic Segmentation,
IP(31), 2022, pp. 1406-1417.
IEEE DOI 2202
Annotations, Semantics, Image segmentation, Training, Benchmark testing, Predictive models, Convolution, multiple instance learning BibRef

Ge, C.[Ce], Sun, H.F.[Hai-Feng], Song, Y.Z.[Yi-Zhe], Ma, Z.Y.[Zhan-Yu], Liao, J.X.[Jian-Xin],
Exploring Local Detail Perception for Scene Sketch Semantic Segmentation,
IP(31), 2022, pp. 1447-1461.
IEEE DOI 2202
Image segmentation, Semantics, Automobiles, Layout, Task analysis, Feature extraction, Airplanes, Sketch dataset, scene sketch, local detail perception BibRef

Li, Q.[Qi], Yang, W.X.[Wei-Xiang], Liu, W.X.[Wen-Xi], Yu, Y.L.[Yuan-Long], He, S.F.[Sheng-Feng],
From Contexts to Locality: Ultra-high Resolution Image Segmentation via Locality-aware Contextual Correlation,
ICCV21(7232-7241)
IEEE DOI 2203
Image segmentation, Image resolution, Correlation, Codes, Computational modeling, Semantics, Segmentation, Scene analysis and understanding BibRef

Strudel, R.[Robin], Garcia, R.[Ricardo], Laptev, I.[Ivan], Schmid, C.[Cordelia],
Segmenter: Transformer for Semantic Segmentation,
ICCV21(7242-7252)
IEEE DOI 2203
Image segmentation, Image coding, Semantics, Transformers, Encoding, Decoding, Segmentation, grouping and shape, Visual reasoning and logical representation BibRef

Sakaridis, C.[Christos], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation,
PAMI(44), No. 6, June 2022, pp. 3139-3153.
IEEE DOI 2205
BibRef
Earlier:
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation,
ICCV19(7373-7382)
IEEE DOI 2004
Semantics, Image segmentation, Adaptation models, Annotations, Task analysis, Measurement, Lighting, Domain adaptation, curriculum learning. image annotation, learning (artificial intelligence), Uncertainty BibRef

Tan, Z.T.[Zhen-Tao], Chen, D.D.[Dong-Dong], Chu, Q.[Qi], Chai, M.L.[Meng-Lei], Liao, J.[Jing], He, M.M.[Ming-Ming], Yuan, L.[Lu], Hua, G.[Gang], Yu, N.H.[Neng-Hai],
Efficient Semantic Image Synthesis via Class-Adaptive Normalization,
PAMI(44), No. 9, September 2022, pp. 4852-4866.
IEEE DOI 2208
Semantics, Image synthesis, Modulation, Image segmentation, Task analysis, Generators, Visualization, Semantic image synthesis, Positional encoding BibRef

Chen, M.H.[Ming-Hong], Shu, R.J.[Rui-Jun], Zhu, D.C.[Dong-Chen], Li, J.[Jiamao], Zhang, X.L.[Xiao-Lin],
CSF: Closed-mask-guided semantic fusion method for semantic perception of unknown scenes,
PRL(161), 2022, pp. 101-107.
Elsevier DOI 2209
Semantic fusion, Information entropy, Mask completion, Semantic segmentation BibRef

Rosas-Arias, L.[Leonel], Benitez-Garcia, G.[Gibran], Portillo-Portillo, J.[José], Olivares-Mercado, J.[Jesus], Sánchez-Pérez, G.[Gabriel], Yanai, K.[Keiji],
FASSD-Net: Fast and Accurate Real-Time Semantic Segmentation for Embedded Systems,
ITS(23), No. 9, September 2022, pp. 14349-14360.
IEEE DOI 2209
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions,
ICPR21(2264-2271)
IEEE DOI 2105
Real-time systems, Semantics, Convolutional codes, Embedded systems, Decoding, Task analysis, Image segmentation. Convolutional codes, Image resolution, Quantization (signal), Real-time systems BibRef

Zhang, X.[Xian], Li, Q.[Qiang], Quan, Z.B.[Zhi-Bin], Yang, W.K.[Wan-Kou],
Pyramid Geometric Consistency Learning For Semantic Segmentation,
PR(133), 2023, pp. 109020.
Elsevier DOI 2210
Semantic segmentation, Consistency learning, Supervised contrastive learning BibRef

Gao, G.Y.[Guang-Yu], Fang, Z.Y.[Zhi-Yuan], Han, C.[Cen], Wei, Y.C.[Yun-Chao], Liu, C.H.[Chi Harold], Yan, S.C.[Shui-Cheng],
DRNet: Double Recalibration Network for Few-Shot Semantic Segmentation,
IP(31), 2022, pp. 6733-6746.
IEEE DOI 2211
Convolution, Task analysis, Prototypes, Heating systems, Annotations, Image color analysis, Robustness, Few-shot learning, weakly-supervised learning BibRef

Li, Z.[Zechao], Sun, Y.P.[Yan-Ping], Zhang, L.Y.[Li-Yan], Tang, J.H.[Jin-Hui],
CTNet: Context-Based Tandem Network for Semantic Segmentation,
PAMI(44), No. 12, December 2022, pp. 9904-9917.
IEEE DOI 2212
Semantics, Image segmentation, Feature extraction, Correlation, Computational modeling, Task analysis, Convolution, tandem network BibRef

Quan, Y.[Yu], Zhang, D.[Dong], Zhang, L.Y.[Li-Yan], Tang, J.H.[Jin-Hui],
Centralized Feature Pyramid for Object Detection,
IP(32), 2023, pp. 4341-4354.
IEEE DOI 2308
Feature extraction, Visualization, Object detection, Regulation, Transformers, Task analysis, Detectors, Feature pyramid, long-range dependencies BibRef

Zhou, Q.[Quan], Qiang, Y.[Yong], Mo, Y.W.[Yu-Wei], Wu, X.[Xiaofu], Latecki, L.J.[Longin Jan],
BANet: Boundary-Assistant Encoder-Decoder Network for Semantic Segmentation,
ITS(23), No. 12, December 2022, pp. 25259-25270.
IEEE DOI 2212
Semantics, Image segmentation, Feature extraction, Convolution, Shape, Task analysis, Decoding, Semantic segmentation, dilated-ResNet101 BibRef

Tian, Z.T.[Zhuo-Tao], Chen, P.G.[Peng-Guang], Lai, X.[Xin], Jiang, L.[Li], Liu, S.[Shu], Zhao, H.S.[Heng-Shuang], Yu, B.[Bei], Yang, M.C.[Ming-Chang], Jia, J.Y.[Jia-Ya],
Adaptive Perspective Distillation for Semantic Segmentation,
PAMI(45), No. 2, February 2023, pp. 1372-1387.
IEEE DOI 2301
Semantics, Feature extraction, Training, Predictive models, Adaptation models, Avalanche photodiodes, Context modeling, semantic segmentation BibRef

Verelst, T.[Thomas], Tuytelaars, T.[Tinne],
SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation,
PAMI(45), No. 2, February 2023, pp. 2400-2411.
IEEE DOI 2301
Image segmentation, Complexity theory, Task analysis, Computational efficiency, Semantics, Computational modeling, Costs, semantic segmentation BibRef

Seifi, S.[Soroush], Tuytelaars, T.[Tinne],
Attend and Segment: Attention Guided Active Semantic Segmentation,
ECCV20(XXV:305-321).
Springer DOI 2011
BibRef

Cai, Y.X.[Yu-Xiang], Yu, Y.[Yuanlong], Jiang, W.J.[Wei-Jie], Chen, R.[Rong], Zheng, W.T.[Wei-Tao], Wu, X.[Xi], Su, R.J.[Ren-Jie],
A novel decoder based on Bayesian rules for task-driven object segmentation,
IET-IPR(17), No. 3, 2023, pp. 832-848.
DOI Link 2303
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

Jin, Z.C.[Zhen-Chao], Yu, D.D.[Dong-Dong], Yuan, Z.H.[Ze-Huan], Yu, L.Q.[Le-Quan],
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation,
PAMI(45), No. 5, May 2023, pp. 5988-6005.
IEEE DOI 2304
Image segmentation, Semantics, Memory modules, Task analysis, Iterative methods, Benchmark testing, Transformers, video semantic segmentation BibRef

Jin, Z.C.[Zhen-Chao], Gong, T.[Tao], Yu, D.D.[Dong-Dong], Chu, Q.[Qi], Wang, J.[Jian], Wang, C.H.[Chang-Hu], Shao, J.[Jie],
Mining Contextual Information Beyond Image for Semantic Segmentation,
ICCV21(7211-7221)
IEEE DOI 2203
Training, Image segmentation, Lips, Semantics, Memory modules, Data aggregation, Segmentation, grouping and shape, BibRef

Huang, Z.L.[Zi-Long], Wang, X.G.[Xing-Gang], Wei, Y.C.[Yun-Chao], Huang, L.C.[Li-Chao], Shi, H.[Humphrey], Liu, W.Y.[Wen-Yu], Huang, T.S.[Thomas S.],
CCNet: Criss-Cross Attention for Semantic Segmentation,
PAMI(45), No. 6, June 2023, pp. 6896-6908.
IEEE DOI 2305
Semantics, Task analysis, Aggregates, Benchmark testing, Lips, Image segmentation, Context modeling, Semantic segmentation, context modeling BibRef

Gao, S.H.[Shang-Hua], Li, Z.Y.[Zhong-Yu], Yang, M.H.[Ming-Hsuan], Cheng, M.M.[Ming-Ming], Han, J.W.[Jun-Wei], Torr, P.H.S.[Philip H.S.],
Large-Scale Unsupervised Semantic Segmentation,
PAMI(45), No. 6, June 2023, pp. 7457-7476.
IEEE DOI 2305
Task analysis, Semantics, Benchmark testing, Shape, Annotations, Representation learning, Training, Large-scale, unsupervised BibRef

Liu, J.S.[Jin-Shi], Jiang, Z.H.[Zhao-Hui], Cao, T.[Ting], Chen, Z.W.[Zhi-Wen], Zhang, C.[Chaobo], Gui, W.H.[Wei-Hua],
Generated Pseudo-Labels Guided by Background Skeletons for Overcoming Under-Segmentation in Overlapping Particle Objects,
CirSysVideo(33), No. 6, June 2023, pp. 2906-2919.
IEEE DOI 2306
Image segmentation, Skeleton, Training, Data models, Semantic segmentation, Task analysis, Training data, leaf vertices BibRef

Wu, D.Y.[Dong-Yue], Guo, Z.[Zilin], Li, A.[Aoyan], Yu, C.Q.[Chang-Qian], Gao, C.X.[Chang-Xin], Sang, N.[Nong],
Conditional Boundary Loss for Semantic Segmentation,
IP(32), 2023, pp. 3717-3731.
IEEE DOI 2307
Task analysis, Semantic segmentation, Optimization, Semantics, Training, Noise measurement, Feature extraction, boundary BibRef

Hu, X.G.[Xue-Gang], Xu, S.H.[Shu-Han], Jing, L.Y.[Li-Yuan],
Lightweight attention-guided redundancy-reuse network for real-time semantic segmentation,
IET-IPR(17), No. 9, 2023, pp. 2649-2658.
DOI Link 2307
convolutional neural nets, image segmentation, neural net architecture BibRef

An, T.H.[Taeg-Hyun], Kang, J.[Jungyu], Min, K.W.[Kyoung-Wook],
Network adaptation for color image semantic segmentation,
IET-IPR(17), No. 10, 2023, pp. 2972-2983.
DOI Link 2308
computer vision, image colour analysis, image segmentation BibRef

Zhang, F.Y.[Feng-Yu], Panahi, A.[Ashkan], Gao, G.J.[Guang-Jun],
FsaNet: Frequency Self-Attention for Semantic Segmentation,
IP(32), 2023, pp. 4757-4772.
IEEE DOI 2309
BibRef

Lin, F.J.[Fang-Jian], Wu, S.T.[Si-Tong], Ma, Y.Z.[Yi-Zhe], Tian, S.W.[Sheng-Wei],
Full-scale Selective Transformer for Semantic Segmentation,
ACCV22(VII:310-326).
Springer DOI 2307
BibRef

Dong, A.[Aimei], Liu, S.[Sidi],
Research on Multi-task Semantic Segmentation Based on Attention and Feature Fusion Method,
MMMod23(II: 362-373).
Springer DOI 2304
BibRef

Zabari, N.[Nir], Hoshen, Y.[Yedid],
Open-vocabulary Semantic Segmentation Using Test-time Distillation,
LLID22(56-72).
Springer DOI 2304
BibRef

Zheng, Z.S.[Zi-Shuo], Lin, C.Y.[Chun-Yu], Nie, L.[Lang], Liao, K.[Kang], Shen, Z.J.[Zhi-Jie], Zhao, Y.[Yao],
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation,
WACV23(4490-4499)
IEEE DOI 2302
Visualization, Image coding, Semantic segmentation, Bidirectional control, Distortion, Feature extraction, Virtual/augmented reality BibRef

Shen, F.Y.[Feng-Yi], Pataki, Z.[Zador], Gurram, A.[Akhil], Liu, Z.Y.[Zi-Yuan], Wang, H.[He], Knoll, A.[Alois],
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation,
WACV23(3255-3265)
IEEE DOI 2302
Training, Adaptation models, Codes, Semantic segmentation, Semantics, Pipelines, Algorithms: Machine learning architectures, visual reasoning BibRef

Sacha, M.[Mikolaj], Rymarczyk, D.[Dawid], Struski, L.[Lukasz], Tabor, J.[Jacek], Zielinski, B.[Bartosz],
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts,
WACV23(1481-1492)
IEEE DOI 2302
Training, Adaptation models, Semantic segmentation, Semantics, Prototypes, Algorithms: Explainable, fair, accountable, visual reasoning BibRef

Kanakis, M.[Menelaos], Huang, T.E.[Thomas E.], Brüggemann, D.[David], Yu, F.[Fisher], Van Gool, L.J.[Luc J.],
Composite Learning for Robust and Effective Dense Predictions,
WACV23(2298-2307)
IEEE DOI 2302
Training, Semantic segmentation, Estimation, Multitasking, Robustness, Labeling, Algorithms: Machine learning architectures, and algorithms (including transfer) BibRef

Brüggemann, D.[David], Sakaridis, C.[Christos], Truong, P.[Prune], Van Gool, L.J.[Luc J.],
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions,
WACV23(3173-3183)
IEEE DOI 2302
Training, Visualization, Uncertainty, Semantic segmentation, Semantics, Refining, Robotics BibRef

Endo, K.[Kazuki], Tanaka, M.[Masayuki], Okutomi, M.[Masatoshi],
Semantic Segmentation of Degraded Images Using Layer-Wise Feature Adjustor,
WACV23(3204-3212)
IEEE DOI 2302
Degradation, Knowledge engineering, Image recognition, Semantic segmentation, Surveillance, Transform coding, segmentation) 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

Themyr, L.[Loic], Rambour, C.[Clément], Thome, N.[Nicolas], Collins, T.[Toby], Hostettler, A.[Alexandre],
Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation,
WACV23(3223-3232)
IEEE DOI 2302
Training, Visualization, Solid modeling, Image resolution, Semantic segmentation, Transformers BibRef

Dong, L.[Lusen], Wang, F.[Fei], Zheng, J.[Jin],
Context and Apparent Features Aggregation Network for Semantic Segmentation,
ICPR22(3858-3864)
IEEE DOI 2212
Convolution, Semantic segmentation, Aggregates, Image edge detection, Neural networks, Feature extraction, Transformers BibRef

Nunes, I.[Ian], Pereira, M.B.[Matheus B.], Oliveira, H.[Hugo], dos Santos, J.A.[Jefersson A.], Poggi, M.[Marcus],
Conditional Reconstruction for Open-Set Semantic Segmentation,
ICIP22(946-950)
IEEE DOI 2211
Adaptation models, Semantics, Time series analysis, Data integration, Decoding, Task analysis, Image reconstruction, open world BibRef

Wang, Z.C.[Zi-Chao], Jiang, Z.Y.[Zhi-Yu], Yuan, Y.[Yuan],
Prototype Queue Learning for Multi-Class Few-Shot Semantic Segmentation,
ICIP22(1721-1725)
IEEE DOI 2211
Prototypes, Self-supervised learning, Feature extraction, Task analysis, Few-shot segmentation, Multi-class segmentation, Semantic segmentation BibRef

Kwon, H.[Hyeongjun], Song, T.[Taeyong], Kim, S.[Sunok], Sohn, K.H.[Kwang-Hoon],
Mask-Guided Attention and Episode Adaptive Weights for Few-Shot Segmentation,
ICIP22(2611-2615)
IEEE DOI 2211
Training, Image segmentation, Adaptation models, Benchmark testing, Feature extraction, Data mining, Few-shot segmentation, adaptive learning BibRef

Yang, X.H.[Xin-Hao], Ma, L.Y.[Li-Yan], Zhou, Y.[Yang], Peng, Y.[Yan], Xie, S.R.[Shao-Rong],
Prior Semantic Harmonization Network for Few-Shot Semantic Segmentation,
ICIP22(1126-1130)
IEEE DOI 2211
Correlation, Semantics, Few-shot segmentation, Semantic harmonization, Feature activation, Hierarchical aggregation BibRef

Wang, B.[Bo], Wang, S.[Shiang], Yuan, C.F.[Chun-Feng], Wu, Z.H.[Zhong-Hai], Li, B.[Bing], Hu, W.M.[Wei-Ming], Xiong, J.[Jeffrey],
Learnable Pixel Clustering Via Structure and Semantic Dual Constraints for Unsupervised Image Segmentation,
ICIP22(1041-1045)
IEEE DOI 2211
Representation learning, Image segmentation, Smoothing methods, Annotations, Semantics, Proposals, Task analysis, image segmentation, mutual information maximization BibRef

Kuhn, C.B.[Christopher B.], Hofbauer, M.[Markus], Petrovic, G.[Goran], Steinbach, E.[Eckehard],
Reverse Error Modeling for Improved Semantic Segmentation,
ICIP22(106-110)
IEEE DOI 2211
Image coding, Semantics, Predictive models, Data models, Image reconstruction, Testing, Semantic Segmentation, Error Correction BibRef

Dong, J.[Jianan], Guo, J.[Jichang], Yue, H.H.[Hui-Hui], Gao, H.[Huan],
EANET: Efficient Attention-Augmented Network for Real-Time Semantic Segmentation,
ICIP22(3968-3972)
IEEE DOI 2211
Strips, Costs, Semantics, Graphics processing units, Real-time systems, Mobile handsets, Task analysis, Multi-level Features BibRef

Rossetti, S.[Simone], Zappia, D.[Damiano], Sanzari, M.[Marta], Schaerf, M.[Marco], Pirri, F.[Fiora],
Max Pooling with Vision Transformers Reconciles Class and Shape in Weakly Supervised Semantic Segmentation,
ECCV22(XXX:446-463).
Springer DOI 2211
BibRef

Qin, J.[Jie], Wu, J.[Jie], Li, M.[Ming], Xiao, X.F.[Xue-Feng], Zheng, M.[Min], Wang, X.G.[Xin-Gang],
Multi-granularity Distillation Scheme Towards Lightweight Semi-supervised Semantic Segmentation,
ECCV22(XXX:481-498).
Springer DOI 2211
BibRef

Xu, M.D.[Meng-De], Zhang, Z.[Zheng], Wei, F.Y.[Fang-Yun], Lin, Y.T.[Yu-Tong], Cao, Y.[Yue], Hu, H.[Han], Bai, X.[Xiang],
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model,
ECCV22(XXIX:736-753).
Springer DOI 2211
BibRef

Lin, Z.H.[Zi-Han], Wang, Z.L.[Zi-Lei], Zhang, Y.X.[Yi-Xin],
Continual Semantic Segmentation via Structure Preserving and Projected Feature Alignment,
ECCV22(XXIX:345-361).
Springer DOI 2211
BibRef

Pissas, T.[Theodoros], Ravasio, C.S.[Claudio S.], da Cruz, L.[Lyndon], Bergeles, C.[Christos],
Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation,
ECCV22(XXIX:413-429).
Springer DOI 2211
BibRef

Wu, T.[Tong], Gao, G.Y.[Guang-Yu], Huang, J.[Junshi], Wei, X.L.[Xiao-Lin], Wei, X.M.[Xiao-Ming], Liu, C.H.[Chi Harold],
Adaptive Spatial-BCE Loss for Weakly Supervised Semantic Segmentation,
ECCV22(XXIX:199-216).
Springer DOI 2211
BibRef

Yin, Z.Y.[Zhao-Yuan], Wang, P.[Pichao], Wang, F.[Fan], Xu, X.Z.[Xian-Zhe], Zhang, H.L.[Han-Ling], Li, H.[Hao], Jin, R.[Rong],
TransFGU: A Top-Down Approach to Fine-Grained Unsupervised Semantic Segmentation,
ECCV22(XXIX:73-89).
Springer DOI 2211
BibRef

Shi, B.[Bowen], Jiang, D.S.[Dong-Sheng], Zhang, X.P.[Xiao-Peng], Li, H.[Han], Dai, W.[Wenrui], Zou, J.[Junni], Xiong, H.K.[Hong-Kai], Tian, Q.[Qi],
A Transformer-Based Decoder for Semantic Segmentation with Multi-level Context Mining,
ECCV22(XXVIII:624-639).
Springer DOI 2211
BibRef

Huang, Y.[Ye], Kang, D.[Di], Chen, L.[Liang], Zhe, X.F.[Xue-Fei], Jia, W.J.[Wen-Jing], Bao, L.C.[Lin-Chao], He, X.J.[Xiang-Jian],
CAR: Class-Aware Regularizations for Semantic Segmentation,
ECCV22(XXVIII:518-534).
Springer DOI 2211
BibRef

Liu, Q.D.[Quan-De], Wen, Y.P.[You-Peng], Han, J.H.[Jian-Hua], Xu, C.J.[Chun-Jing], Xu, H.[Hang], Liang, X.D.[Xiao-Dan],
Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding,
ECCV22(XX:275-292).
Springer DOI 2211
BibRef

Wang, F.[Feng], Wang, H.Y.[Hui-Yu], Wei, C.[Chen], Yuille, A.L.[Alan L.], Shen, W.[Wei],
CP 2: Copy-Paste Contrastive Pretraining for Semantic Segmentation,
ECCV22(XXX:499-515).
Springer DOI 2211
BibRef

Yang, F.Y.[Feng-Yu], Ma, C.Y.[Chen-Yang],
Sparse and Complete Latent Organization for Geospatial Semantic Segmentation,
CVPR22(1799-1808)
IEEE DOI 2210
Image segmentation, Computational modeling, Semantics, Prototypes, Organizations, Geospatial analysis, Photogrammetry and remote sensing BibRef

Brempong, E.A.[Emmanuel Asiedu], Kornblith, S.[Simon], Chen, T.[Ting], Parmar, N.[Niki], Minderer, M.[Matthias], Norouzi, M.[Mohammad],
Denoising Pretraining for Semantic Segmentation,
L3D-IVU22(4174-4185)
IEEE DOI 2210
Training, Image segmentation, Noise reduction, Semantics, Supervised learning, Transformers, Probabilistic logic BibRef

Bär, A.[Andreas], Klingner, M.[Marvin], Löhdefink, J.[Jonas], Hüger, F.[Fabian], Schlicht, P.[Peter], Fingscheidt, T.[Tim],
Performance Prediction for Semantic Segmentation by a Self-Supervised Image Reconstruction Decoder,
WAD22(4398-4407)
IEEE DOI 2210
Training, Image segmentation, Semantics, Supervised learning, Training data, Benchmark testing, Sensors BibRef

Mehta, D.[Dushyant], Skliar, A.[Andrii], Ben Yahia, H.[Haitam], Borse, S.[Shubhankar], Porikli, F.M.[Fatih M.], Habibian, A.[Amirhossein], Blankevoort, T.[Tijmen],
Simple and Efficient Architectures for Semantic Segmentation,
ECV22(2627-2635)
IEEE DOI 2210
Image segmentation, Head, Semantics, Graphics processing units, Computer architecture, Hardware, Distance measurement BibRef

Liu, S.A.[Sun-Ao], Xie, H.T.[Hong-Tao], Xu, H.[Hai], Zhang, Y.D.[Yong-Dong], Tian, Q.[Qi],
Partial Class Activation Attention for Semantic Segmentation,
CVPR22(16815-16824)
IEEE DOI 2210
Location awareness, Image segmentation, Codes, Semantics, Benchmark testing, Pattern recognition, grouping and shape analysis BibRef

Gu, J.Q.[Jia-Qi], Kwon, H.[Hyoukjun], Wang, D.[Dilin], Ye, W.[Wei], Li, M.[Meng], Chen, Y.H.[Yu-Hsin], Lai, L.Z.[Liang-Zhen], Chandra, V.[Vikas], Pan, D.Z.[David Z.],
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation,
CVPR22(12084-12093)
IEEE DOI 2210
Representation learning, Architecture, Semantics, Redundancy, Computer architecture, Object detection, grouping and shape analysis BibRef

Ding, J.[Jian], Xue, N.[Nan], Xia, G.S.[Gui-Song], Dai, D.X.[Deng-Xin],
Decoupling Zero-Shot Semantic Segmentation,
CVPR22(11573-11582)
IEEE DOI 2210
Training, Image segmentation, Codes, Shape, Semantics, Pattern recognition, Segmentation, grouping and shape analysis BibRef

Kwon, D.[Donghyeon], Kwak, S.[Suha],
Semi-supervised Semantic Segmentation with Error Localization Network,
CVPR22(9947-9957)
IEEE DOI 2210
Training, Location awareness, Degradation, Heart, Image resolution, Semisupervised learning, Pattern recognition, grouping and shape analysis 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

Wang, Y.C.[Yu-Chao], Wang, H.C.[Hao-Chen], Shen, Y.J.[Yu-Jun], Fei, J.J.[Jing-Jing], Li, W.[Wei], Jin, G.Q.[Guo-Qiang], Wu, L.W.[Li-Wei], Zhao, R.[Rui], Le, X.[Xinyi],
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels,
CVPR22(4238-4247)
IEEE DOI 2210
Training, Visualization, Shape, Semantics, Predictive models, Semisupervised learning, Solids, Segmentation, Self- semi- meta- unsupervised learning BibRef

Zhao, Y.Y.[Yu-Yang], Zhong, Z.[Zhun], Sebe, N.[Nicu], Lee, G.H.[Gim Hee],
Novel Class Discovery in Semantic Segmentation,
CVPR22(4330-4339)
IEEE DOI 2210
Training, Image segmentation, Uncertainty, Shape, Semantics, Self-supervised learning, Benchmark testing, Segmentation, Self- semi- meta- Transfer/low-shot/long-tail learning 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

Zhang, Y.F.[Yi-Fan], Pang, B.[Bo], Lu, C.[Cewu],
Semantic Segmentation by Early Region Proxy,
CVPR22(1248-1258)
IEEE DOI 2210
Image segmentation, Shape, Computational modeling, Semantics, Layout, Predictive models, Transformers, Segmentation, grouping and shape analysis BibRef

Ke, T.W.[Tsung-Wei], Hwang, J.J.[Jyh-Jing], Guo, Y.H.[Yun-Hui], Wang, X.D.[Xu-Dong], Yu, S.X.[Stella X.],
Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers,
CVPR22(2561-2571)
IEEE DOI 2210
Representation learning, Image segmentation, Visualization, Semantics, Benchmark testing, Transformers, Segmentation, Self- semi- meta- unsupervised learning BibRef

Ratajczak, R.[Rémi], Crispim, C.[Carlos], Fervers, B.[Béatrice], Faure, E.[Elodie], Tougne, L.[Laure],
Semantic Segmentation Post-processing with Colorized Pairwise Potentials and Deep Edges,
IPTA20(1-6)
IEEE DOI 2206
Image segmentation, Image color analysis, Image edge detection, Semantics, Gray-scale, Tools, Task analysis, semantic segmentation, deep edges BibRef

Jin, Z.C.[Zhen-Chao], Liu, B.[Bin], Chu, Q.[Qi], Yu, N.H.[Neng-Hai],
ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation,
ICCV21(7169-7178)
IEEE DOI 2203
Image segmentation, Visualization, Lips, Aggregates, Semantics, Benchmark testing, Segmentation, grouping and shape, BibRef

Zhang, Y.[Yu], Zhang, C.B.[Chang-Bin], Jiang, P.T.[Peng-Tao], Cheng, M.M.[Ming-Ming], Mao, F.[Feng],
Personalized Image Semantic Segmentation,
ICCV21(10529-10539)
IEEE DOI 2203
Image segmentation, Correlation, Codes, Semantics, Datasets and evaluation, Segmentation, grouping and shape BibRef

Jain, S.[Shipra], Paudel, D.P.[Danda Pani], Danelljan, M.[Martin], Van Gool, L.J.[Luc J.],
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU,
ICCV21(7406-7416)
IEEE DOI 2203
Training, Image segmentation, Computational modeling, Scalability, Semantics, Graphics processing units, Object detection, Representation learning BibRef

Wang, W.G.[Wen-Guan], Zhou, T.F.[Tian-Fei], Yu, F.[Fisher], Dai, J.F.[Ji-Feng], Konukoglu, E.[Ender], Van Gool, L.J.[Luc J.],
Exploring Cross-Image Pixel Contrast for Semantic Segmentation,
ICCV21(7283-7293)
IEEE DOI 2203
Training, Representation learning, Measurement, Image segmentation, Semantics, Training data, Optical character recognition software, Scene analysis and understanding BibRef

Hsiao, C.W.[Chi-Wei], Sun, C.[Cheng], Chen, H.T.[Hwann-Tzong], Sun, M.[Min],
Specialize and Fuse: Pyramidal Output Representation for Semantic Segmentation,
ICCV21(7117-7126)
IEEE DOI 2203
Fuses, Aggregates, Semantics, Task analysis, Segmentation, grouping and shape BibRef

Zhang, D.[Dong], Zhang, H.[Hanwang], Tang, J.H.[Jin-Hui], Hua, X.S.[Xian-Sheng], Sun, Q.[Qianru],
Self-Regulation for Semantic Segmentation,
ICCV21(6933-6943)
IEEE DOI 2203
Training, Visualization, Computational modeling, Semantics, Neural networks, Performance gain, Segmentation, Scene analysis and understanding BibRef

Aakerberg, A.[Andreas], Johansen, A.S.[Anders S.], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.],
Semantic Segmentation Guided Real-World Super-Resolution,
RWSurvil22(449-458)
IEEE DOI 2202
Degradation, Training, Image segmentation, Adaptation models, Visualization, Image color analysis, Superresolution BibRef

Garcia, R.L., Happ, P.N., Feitosa, R.Q.,
Large Scale Semantic Segmentation of Virtual Environments to Facilitate Corrosion Management,
ISPRS21(B2-2021: 465-470).
DOI Link 2201
BibRef

Pang, Y.T.[Yu-Ting], Chang, J.[Jui], Hsu, C.T.[Chiou-Ting],
Self-Guided Adversarial Learning for Domain Adaptive Semantic Segmentation,
ICIP21(2249-2253)
IEEE DOI 2201
Learning systems, Image segmentation, Adaptation models, Semantics, Benchmark testing, Reliability, Unsupervised domain adaptation, self-guided adversarial learning BibRef

Shiau, Z.Y.[Zu-Yun], Lin, W.W.[Wei-Wei], Lin, C.S.[Ci-Siang], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation,
ICIP21(2244-2248)
IEEE DOI 2201
Adaptation models, Visualization, Image segmentation, Semantics, Benchmark testing, Feature extraction, domain generalization BibRef

Lou, A.[Ange], Loew, M.[Murray],
CFPNET: Channel-Wise Feature Pyramid for Real-Time Semantic Segmentation,
ICIP21(1894-1898)
IEEE DOI 2201
Performance evaluation, Image segmentation, Convolution, Semantics, Graphics processing units, Real-time semantic segmentation, CFPNet BibRef

Lee, H.J.[Hong Joo], Ro, Y.M.[Yong Man],
Adversarially Robust Multi-Sensor Fusion Model Training Via Random Feature Fusion for Semantic Segmentation,
ICIP21(339-343)
IEEE DOI 2201
Training, Image segmentation, Semantics, Data integration, Object detection, Feature extraction, Multi-sensor data fusion, random feature fusion BibRef

Aakerberg, A.[Andreas], Johansen, A.S.[Anders S.], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.],
Single-Loss Multi-task Learning For Improving Semantic Segmentation Using Super-Resolution,
CAIP21(II:403-411).
Springer DOI 2112
BibRef

Zhu, F.R.[Fang-Rui], Zhu, Y.[Yi], Zhang, L.[Li], Wu, C.R.[Chong-Ruo], Fu, Y.W.[Yan-Wei], Li, M.[Mu],
A Unified Efficient Pyramid Transformer for Semantic Segmentation,
VSPW21(2667-2677)
IEEE DOI 2112
Adaptation models, Image segmentation, Computational modeling, Semantics, Benchmark testing BibRef

Dhingra, N.[Naina], Chogovadze, G.[George], Kunz, A.[Andreas],
Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network,
GSP-CV21(865-875)
IEEE DOI 2112
Computational modeling, Semantics, Refining, Computer architecture, Prediction algorithms 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

Holder, C.J.[Christopher J.], Shafique, M.[Muhammad],
Efficient Uncertainty Estimation in Semantic Segmentation via Distillation,
AVVision21(3080-3087)
IEEE DOI 2112
Training, Image segmentation, Uncertainty, Computational modeling, Semantics, Predictive models, Data models BibRef

Cho, J.H.[Jang Hyun], Mall, U.[Utkarsh], Bala, K.[Kavita], Hariharan, B.[Bharath],
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering,
CVPR21(16789-16799)
IEEE DOI 2111
Training, Visualization, Image segmentation, Codes, Clustering methods, Semantics BibRef

Huynh, C.[Chuong], Tran, A.T.[Anh Tuan], Luu, K.[Khoa], Hoai, M.[Minh],
Progressive Semantic Segmentation,
CVPR21(16750-16759)
IEEE DOI 2111
Image segmentation, Image resolution, Codes, Magnetic separation, Magnetic resonance imaging, Semantics 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

Teja, S.P.[S. Prabhu], Fleuret, F.[François],
Uncertainty Reduction for Model Adaptation in Semantic Segmentation,
CVPR21(9608-9618)
IEEE DOI 2111
Image segmentation, Adaptation models, Uncertainty, Semantics, Urban areas, Feature extraction, Data models BibRef

Li, S.[Shuang], Gong, K.X.[Kai-Xiong], Liu, C.H.[Chi Harold], Wang, Y.L.[Yu-Lin], Qiao, F.[Feng], Cheng, X.[Xinjing],
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition,
CVPR21(5208-5217)
IEEE DOI 2111
Training, Visualization, Semantics, Supervised learning, Training data, Benchmark testing BibRef

Yang, S.[Sibei], Xia, M.[Meng], Li, G.B.[Guan-Bin], Zhou, H.Y.[Hong-Yu], Yu, Y.Z.[Yi-Zhou],
Bottom-Up Shift and Reasoning for Referring Image Segmentation,
CVPR21(11261-11270)
IEEE DOI 2111
Location awareness, Visualization, Image segmentation, Fuses, Message passing, Computational modeling BibRef

Ma, H.Y.[Hao-Yu], Lin, X.R.[Xiang-Ru], Wu, Z.F.[Zi-Feng], Yu, Y.Z.[Yi-Zhou],
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization,
CVPR21(4050-4059)
IEEE DOI 2111
Image segmentation, Adaptation models, Annotations, Computational modeling, Semantics, Pipelines BibRef

Hänsch, R.[Ronny],
Looking Outside the Box: The Role of Context in Random Forest Based Semantic Segmentation of PolSAR Images,
GCPR20(260-274).
Springer DOI 2110
BibRef

Zatsarynna, O.[Olga], Sawatzky, J.[Johann], Gall, J.[Juergen],
Discovering Latent Classes for Semi-supervised Semantic Segmentation,
GCPR20(202-217).
Springer DOI 2110
BibRef

Theodoridou, C.[Christina], Kargakos, A.[Andreas], Kostavelis, I.[Ioannis], Giakoumis, D.[Dimitrios], Tzovaras, D.[Dimitrios],
Spatially-Constrained Semantic Segmentation with Topological Maps and Visual Embeddings,
CVS21(117-129).
Springer DOI 2109
BibRef

Szabó, A.[Attila], Jamali-Rad, H.[Hadi], Mannava, S.D.[Siva-Datta],
Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic Segmentation,
RCV21(2305-2310)
IEEE DOI 2109
Semantics, Performance analysis, Pattern recognition, Risk management 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

Tavera, A.[Antonio], Cermelli, F.[Fabio], Masone, C.[Carlo], Caputo, B.[Barbara],
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation,
WACV22(1959-1968)
IEEE DOI 2202
Training, Image segmentation, Semantics, Benchmark testing, Task analysis, Standards, Segmentation, Grouping and Shape Scene Understanding BibRef

Fontanel, D.[Dario], Cermelli, F.[Fabio], Mancini, M.[Massimiliano], Caputo, B.[Barbara],
Detecting Anomalies in Semantic Segmentation with Prototypes,
SAIAD21(113-121)
IEEE DOI 2109
Training, Visualization, Computational modeling, Semantics, Prototypes BibRef

Arani, E.[Elahe], Marzban, S.[Shabbir], Pata, A.[Andrei], Zonooz, B.[Bahram],
RGPNet: A Real-Time General Purpose Semantic Segmentation,
WACV21(3008-3017)
IEEE DOI 2106
Training, Performance evaluation, Adaptation models, Computational modeling, Semantics, Green products BibRef

Patel, Y.[Yash], Appalaraju, S.[Srikar], Manmatha, R.,
Saliency Driven Perceptual Image Compression,
WACV21(227-236)
IEEE DOI 2106
Measurement, Image segmentation, Image coding, Computational modeling, Bit rate BibRef

Sun, Z.T.[Zi-Tang], Kamata, S.I.[Sei-Ichiro], Wang, R.J.[Ruo-Jing],
Semantic Segmentation Refinement Using Entropy and Boundary-guided Monte Carlo Sampling and Directed Regional Search,
ICPR21(3931-3938)
IEEE DOI 2105
Monte Carlo methods, Semantics, Logic gates, Prediction algorithms, Search problems, Entropy, Classification algorithms BibRef

Zhang, B.[Bin], Zhao, S.J.[Sheng-Jie], Zhang, R.Q.[Rong-Qing],
Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment,
ICPR21(1912-1917)
IEEE DOI 2105
Image segmentation, Solid modeling, Semantics, Neural networks, Virtual reality, Feature extraction, Robustness BibRef

Zhang, Y.F.[Yi-Fei], Sidibé, D.[Désiré], Morel, O.[Olivier], Meriaudeau, F.[Fabrice],
Multiscale Attention-Based Prototypical Network For Few-Shot Semantic Segmentation,
ICPR21(7372-7378)
IEEE DOI 2105
Training, Image segmentation, Adaptation models, Annotations, Semantics, Prototypes, Feature extraction BibRef

Nie, D.[Dong], Xue, J.[Jia], Ren, X.F.[Xiao-Feng],
Bidirectional Pyramid Networks for Semantic Segmentation,
ACCV20(I:654-671).
Springer DOI 2103
BibRef

Ouni, A.[Achref], Royer, E.[Eric], Chevaldonné, M.[Marc], Dhome, M.[Michel],
A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation,
ISVC20(II:647-657).
Springer DOI 2103
BibRef

Mozaffari, M.H.[M. Hamed], Lee, W.S.[Won-Sook],
Semantic Segmentation with Peripheral Vision,
ISVC20(II:421-429).
Springer DOI 2103
BibRef

Huang, H.[Hang], Zhi, P.[Peng], Zhou, H.R.[Hao-Ran], Zhang, Y.J.[Yu-Jin], Wu, Q.[Qiang], Yong, B.B.[Bin-Bin], Tan, W.J.[Wei-Jun], Zhou, Q.G.[Qing-Guo],
An Efficient Tiny Feature Map Network for Real-time Semantic Segmentation,
ISVC20(II:332-343).
Springer DOI 2103
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

Huang, Y., Tang, Z., Su, K.,
Balance-batch: An Optimized Method for Semantic Segmentation Loss Functions,
CVIDL20(342-346)
IEEE DOI 2102
feature extraction, image classification, image representation, image segmentation, learning (artificial intelligence), loss function BibRef

Jiang, S.L., Li, G., Yao, W., Hong, Z.H., Kuc, T.Y.,
Dual Pyramids Encoder-decoder Network for Semantic Segmentation In Ground and Aerial View Images,
ISPRS20(B2:605-610).
DOI Link 2012
BibRef

Jing, Y.[Ya], Kong, T.[Tao], Wang, W.[Wei], Wang, L.[Liang], Li, L.[Lei], Tan, T.N.[Tie-Niu],
Locate then Segment: A Strong Pipeline for Referring Image Segmentation,
CVPR21(9853-9862)
IEEE DOI 2111
Location awareness, Image segmentation, Visualization, Fuses, Pipelines, Object segmentation, Feature extraction BibRef

Chen, W.L.[Wan-Li], Zhu, X.G.[Xin-Ge], Sun, R.Q.[Ruo-Qi], He, J.J.[Jun-Jun], Li, R.Y.[Rui-Yu], Shen, X.Y.[Xiao-Yong], Yu, B.[Bei],
Tensor Low-rank Reconstruction for Semantic Segmentation,
ECCV20(XVII:52-69).
Springer DOI 2011
BibRef

Hu, H.Z.[Han-Zhe], Ji, D.Y.[De-Yi], Gan, W.H.[Wei-Hao], Bai, S.[Shuai], Wu, W.[Wei], Yan, J.J.[Jun-Jie],
Class-wise Dynamic Graph Convolution for Semantic Segmentation,
ECCV20(XVII:1-17).
Springer DOI 2011
BibRef

Yin, M.H.[Ming-Hao], Yao, Z.L.[Zhu-Liang], Cao, Y.[Yue], Li, X.[Xiu], Zhang, Z.[Zheng], Lin, S.[Stephen], Hu, H.[Han],
Disentangled Non-local Neural Networks,
ECCV20(XV:191-207).
Springer DOI 2011
Code, Segmentation.
WWW Link.
WWW Link. BibRef

Lehman, C., Temel, D., Alregib, G.,
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption,
ICIP20(3239-3243)
IEEE DOI 2011
Robustness, Semantics, Entropy, Training, Estimation, Task analysis, Machine learning, Robustness in Machine Learning, Sigmoid 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, S., Zhou, Q., Liu, J., Wang, J., Fan, Y., Wu, X., Latecki, L.J.,
DCM: A Dense-Attention Context Module For Semantic Segmentation,
ICIP20(1431-1435)
IEEE DOI 2011
Convolution, Feature extraction, Semantics, Data mining, Image segmentation, Kernel, Decoding, Semantic segmentation, Attention BibRef

Liu, J.B.[Jian-Bo], He, J.J.[Jun-Jun], Zhang, J.W.[Jia-Wei], Ren, J.S.[Jimmy S.], Li, H.S.[Hong-Sheng],
Efficientfcn: Holistically-guided Decoding for Semantic Segmentation,
ECCV20(XXVI:1-17).
Springer DOI 2011
BibRef

Wang, Y.K.[Yu-Kang], Zhou, W.[Wei], Jiang, T.[Tao], Bai, X.[Xiang], Xu, Y.C.[Yong-Chao],
Intra-class Feature Variation Distillation for Semantic Segmentation,
ECCV20(VII:346-362).
Springer DOI 2011
BibRef

Michieli, U.[Umberto], Borsato, E.[Edoardo], Rossi, L.[Luca], Zanuttigh, P.[Pietro],
Gmnet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild,
ECCV20(VIII:397-414).
Springer DOI 2011
BibRef

Yang, B.[Boyu], Liu, C.[Chang], Li, B.[Bohao], Jiao, J.B.[Jian-Bin], Ye, Q.X.[Qi-Xiang],
Prototype Mixture Models for Few-shot Semantic Segmentation,
ECCV20(VIII:763-778).
Springer DOI 2011
BibRef

Yuan, Y.H.[Yu-Hui], Chen, X.L.[Xi-Lin], Wang, J.D.[Jing-Dong],
Object-contextual Representations for Semantic Segmentation,
ECCV20(VI:173-190).
Springer DOI 2011
BibRef

Kamann, C.[Christoph], Rother, C.[Carsten],
Increasing the Robustness of Semantic Segmentation Models with Painting-by-numbers,
ECCV20(X:369-387).
Springer DOI 2011
BibRef

Liu, Y.F.[Yong-Fei], Zhang, X.Y.[Xiang-Yi], Zhang, S.Y.[Song-Yang], He, X.M.[Xu-Ming],
Part-aware Prototype Network for Few-Shot Semantic Segmentation,
ECCV20(IX:142-158).
Springer DOI 2011
BibRef

He, Y.[Yang], Rahimian, S.[Shadi], Schiele, B.[Bernt], Fritz, M.[Mario],
Segmentations-leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation,
ECCV20(XXIII:519-535).
Springer DOI 2011
BibRef

Xia, Y.D.[Ying-Da], Zhang, Y.[Yi], Liu, F.Z.[Feng-Ze], Shen, W.[Wei], Yuille, A.L.[Alan L.],
Synthesize Then Compare: Detecting Failures and Anomalies for Semantic Segmentation,
ECCV20(I:145-161).
Springer DOI 2011
BibRef

Luo, W.F.[Wen-Feng], Yang, M.[Meng],
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network,
ECCV20(V:784-800).
Springer DOI 2011
BibRef

Cheng, H.K.[Ho Kei], Chung, J.H.[Ji-Hoon], Tai, Y.W.[Yu-Wing], Tang, C.K.[Chi-Keung],
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement,
CVPR20(8887-8896)
IEEE DOI 2008
Image segmentation, Image resolution, Semantics, Computational modeling, Task analysis, Adaptation models, Feature extraction BibRef

Oberdiek, P., Rottmann, M., Fink, G.A.,
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation,
SAIAD20(1331-1340)
IEEE DOI 2008
Image segmentation, Feature extraction, Semantics, Visualization, Agriculture, Machine learning, Image retrieval BibRef

Zhen, M., Wang, J., Zhou, L., Li, S., Shen, T., Shang, J., Fang, T., Quan, L.,
Joint Semantic Segmentation and Boundary Detection Using Iterative Pyramid Contexts,
CVPR20(13663-13672)
IEEE DOI 2008
Pattern recognition BibRef

Kamann, C., Rother, C.,
Benchmarking the Robustness of Semantic Segmentation Models,
CVPR20(8825-8835)
IEEE DOI 2008
Robustness, Semantics, Computer architecture, Image segmentation, Feature extraction, Benchmark testing, Cameras BibRef

Siddiqui, Y., Valentin, J., Nießner, M.,
ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation,
CVPR20(9430-9440)
IEEE DOI 2008
Entropy, Uncertainty, Semantics, Labeling, Image segmentation, Task analysis, Data models BibRef

Zhang, Y., Qiu, Z., Yao, T., Ngo, C., Liu, D., Mei, T.,
Transferring and Regularizing Prediction for Semantic Segmentation,
CVPR20(9618-9627)
IEEE DOI 2008
Semantics, Image segmentation, Roads, Buildings, Visualization, Labeling, Adaptation models BibRef

Araslanov, N., Roth, S.,
Single-Stage Semantic Segmentation From Image Labels,
CVPR20(4252-4261)
IEEE DOI 2008
Image segmentation, Training, Semantics, Task analysis, Logic gates, Stochastic processes, Decoding BibRef

Wang, L., Li, D., Zhu, Y., Tian, L., Shan, Y.,
Dual Super-Resolution Learning for Semantic Segmentation,
CVPR20(3773-3782)
IEEE DOI 2008
Semantics, Image segmentation, Spatial resolution, Task analysis, Pose estimation, Convolution 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

Lin, P., Sun, P., Cheng, G., Xie, S., Li, X., Shi, J.,
Graph-Guided Architecture Search for Real-Time Semantic Segmentation,
CVPR20(4202-4211)
IEEE DOI 2008
Computer architecture, Microprocessors, Semantics, Convolution, Image segmentation, Real-time systems, Random variables BibRef

Rai, S.N., Balasubramanian, V.N., Subramanian, A., Jawahar, C.V.,
Munich to Dubai: How far is it for Semantic Segmentation?,
WACV20(2988-2997)
IEEE DOI 2006
Image restoration, Image segmentation, Atmospheric modeling, Meteorology, Semantics, Adaptation models, Training BibRef

Du, Y.M.[Yu-Ming], Xiao, Y.[Yang], Lepetit, V.[Vincent],
Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos,
ICCV21(3355-3364)
IEEE DOI 2203
Training, Shape, Video sequences, Training data, Manuals, Object segmentation, Object detection, grouping and shape BibRef

Stekovic, S., Fraundorfer, F., Lepetit, V.[Vincent],
Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning,
WACV20(1843-1852)
IEEE DOI 2006
Image segmentation, Semantics, Predictive models, Semisupervised learning, Task analysis, Training, BibRef

Lin, H.[Hubert], Upchurch, P.[Paul], Bala, K.[Kavita],
Block Annotation: Better Image Annotation With Sub-Image Decomposition,
ICCV19(5289-5299)
IEEE DOI 2004
Generate the data needed to do semantic segmentation. image retrieval, image segmentation, image texture, block annotation, high-quality pixel-level annotations, Quality control BibRef

Kalluri, T., Varma, G., Chandraker, M., Jawahar, C.V.,
Universal Semi-Supervised Semantic Segmentation,
ICCV19(5258-5269)
IEEE DOI 2004
entropy, image segmentation, unsupervised learning, cross-domain unsupervised losses, segmentation datasets, Roads BibRef

Cheng, B., Chen, L., Wei, Y., Zhu, Y., Huang, Z., Xiong, J., Huang, T., Hwu, W., Shi, H., Uiuc, U.,
SPGNet: Semantic Prediction Guidance for Scene Parsing,
ICCV19(5217-5227)
IEEE DOI 2004
feature extraction, image coding, image segmentation, learning (artificial intelligence), pose estimation, Feature extraction BibRef

He, J., Deng, Z., Qiao, Y.,
Dynamic Multi-Scale Filters for Semantic Segmentation,
ICCV19(3561-3571)
IEEE DOI 2004
convolutional neural nets, image filtering, image representation, image segmentation, Dynamic multiscale filters, Computational efficiency BibRef

Pang, Y., Li, Y., Shen, J., Shao, L.,
Towards Bridging Semantic Gap to Improve Semantic Segmentation,
ICCV19(4229-4238)
IEEE DOI 2004
feature extraction, image enhancement, image fusion, image representation, image segmentation, object detection, Convolution BibRef

Zhang, C., Liwicki, S., Smith, W., Cipolla, R.,
Orientation-Aware Semantic Segmentation on Icosahedron Spheres,
ICCV19(3532-3540)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image resolution, image segmentation, Task analysis BibRef

Marin, D., He, Z., Vajda, P., Chatterjee, P., Tsai, S., Yang, F., Boykov, Y.Y.,
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries,
ICCV19(2131-2141)
IEEE DOI 2004
image sampling, image segmentation, learning (artificial intelligence), semantic boundaries, Image resolution BibRef

Nakajima, Y., Kang, B., Saito, H., Kitani, K.,
Incremental Class Discovery for Semantic Segmentation With RGBD Sensing,
ICCV19(972-981)
IEEE DOI 2004
image colour analysis, image representation, image segmentation, learning (artificial intelligence), object recognition, Image color analysis BibRef

Han, Q.Y.[Qiu-Yuan], Zheng, J.[Jin],
Multi-scale Spatial Location Preference for Semantic Segmentation,
MMMod20(I:593-604).
Springer DOI 2003
BibRef

Jiao, J.B.[Jian-Bo], Wei, Y.C.[Yun-Chao], Jie, Z.Q.[Ze-Qun], Shi, H.H.[Hong-Hui], Lau, R.W.H.[Rynson W.H.], Huang, T.S.[Thomas S.],
Geometry-Aware Distillation for Indoor Semantic Segmentation,
CVPR19(2864-2873).
IEEE DOI 2002
BibRef

Liu, Y.F.[Yi-Fan], Chen, K.[Ke], Liu, C.[Chris], Qin, Z.C.[Zeng-Chang], Luo, Z.B.[Zhen-Bo], Wang, J.D.[Jing-Dong],
Structured Knowledge Distillation for Semantic Segmentation,
CVPR19(2599-2608).
IEEE DOI 2002
BibRef

Tian, Z.[Zhi], He, T.[Tong], Shen, C.H.[Chun-Hua], Yan, Y.[Youliang],
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation,
CVPR19(3121-3130).
IEEE DOI 2002
BibRef

Zhou, Y.Z.[Yi-Zhou], Sun, X.Y.[Xiao-Yan], Zha, Z.J.[Zheng-Jun], Zeng, W.J.[Wen-Jun],
Context-Reinforced Semantic Segmentation,
CVPR19(4041-4050).
IEEE DOI 2002
BibRef

Zhang, Z.Y.[Zhen-Yu], Cui, Z.[Zhen], Xu, C.Y.[Chun-Yan], Yan, Y.[Yan], Sebe, N.[Nicu], Yang, J.[Jian],
Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation,
CVPR19(4101-4110).
IEEE DOI 2002
BibRef

Zhang, H.[Hang], Zhang, H.[Han], Wang, C.G.[Chen-Guang], Xie, J.Y.[Jun-Yuan],
Co-Occurrent Features in Semantic Segmentation,
CVPR19(548-557).
IEEE DOI 2002
BibRef

He, T.[Tong], Shen, C.H.[Chun-Hua], Tian, Z.[Zhi], Gong, D.[Dong], Sun, C.M.[Chang-Ming], Yan, Y.[Youliang],
Knowledge Adaptation for Efficient Semantic Segmentation,
CVPR19(578-587).
IEEE DOI 2002
BibRef

Chang, W.L.[Wei-Lun], Wang, H.P.[Hui-Po], Peng, W.H.[Wen-Hsiao], Chiu, W.C.[Wei-Chen],
All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation,
CVPR19(1900-1909).
IEEE DOI 2002
BibRef

Schmitz, M., Brandenburger, W., Mayer, H.,
Semantic Segmentation of Airborne Images and Corresponding Digital Surface Models - Additional Input Data Or Additional Task?,
PIA19(195-200).
DOI Link 1912
BibRef

Huang, Y., Proesmans, M., Georgoulis, S., Van Gool, L.J.,
Uncertainty based model selection for fast semantic segmentation,
MVA19(1-6)
DOI Link 1911
entropy, image segmentation, inference mechanisms, semantic labels, baseline model, reasonable inference speeds, Entropy BibRef

Bevandic, P.[Petra], Krešo, I.[Ivan], Oršic, M.[Marin], Šegvic, S.[Siniša],
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift,
GCPR19(33-47).
Springer DOI 1911
BibRef

Russo, P.[Paolo], Tommasi, T.[Tatiana], Caputo, B.[Barbara],
Towards Multi-source Adaptive Semantic Segmentation,
CIAP19(I:292-301).
Springer DOI 1909
BibRef

Leonardi, M.[Marco], Mazzini, D.[Davide], Schettini, R.[Raimondo],
Training Efficient Semantic Segmentation CNNs on Multiple Datasets,
CIAP19(II:303-314).
Springer DOI 1909
BibRef

Zhuang, P.C.[Peng-Cheng], Sekikawa, Y.[Yusuke], Hara, K.[Kosuke], Saito, H.[Hideo],
Learning an Optimisable Semantic Segmentation Map with Image Conditioned Variational Autoencoder,
CIAP19(II:379-389).
Springer DOI 1909
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

Dias, P.A.[Philipe Ambrozio], Medeiros, H.[Henry],
Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections,
ACCV18(II:131-146).
Springer DOI 1906
BibRef

Falcão, A.X.[Alexandre X.], Bragantini, J.[Jordão],
The Role of Optimum Connectivity in Image Segmentation: Can the Algorithm Learn Object Information During the Process?,
DGCI19(180-194).
Springer DOI 1905
Feature space and image domain linkage. BibRef

Guan, H., Zhang, Z., Tan, T.,
Inception Donut Convolution for Top-down Semantic Segmentation,
ICPR18(2492-2497)
IEEE DOI 1812
convolution, feature extraction, feedforward neural nets, image representation, image segmentation, Streaming media BibRef

Vallurupalli, N., Annamaneni, S., Varma, G., Jawahar, C., Mathew, M., Nagori, S.,
Efficient Semantic Segmentation Using Gradual Grouping,
ECVW18(711-7118)
IEEE DOI 1812
Training, Semantics, Computer architecture, Computational modeling, Sparse matrices, Predictive models, Decoding BibRef

Tsai, Y., Hung, W., Schulter, S., Sohn, K., Yang, M., Chandraker, M.,
Learning to Adapt Structured Output Space for Semantic Segmentation,
CVPR18(7472-7481)
IEEE DOI 1812
Image segmentation, Semantics, Adaptation models, Task analysis, Training, Prediction algorithms, Layout BibRef

Muralikrishnan, S., Kim, V.G., Chaudhuri, S.,
Tags2Parts: Discovering Semantic Regions from Shape Tags,
CVPR18(2926-2935)
IEEE DOI 1812
Shape, Computer architecture, Image segmentation, Convolution, Semantics, Training BibRef

Bilinski, P., Prisacariu, V.[Victor],
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation,
CVPR18(6596-6605)
IEEE DOI 1812
Decoding, Semantics, Image segmentation, Architecture, Computer architecture, Fuses, Spatial resolution BibRef

Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., Agrawal, A.,
Context Encoding for Semantic Segmentation,
CVPR18(7151-7160)
IEEE DOI 1812
Encoding, Semantics, Convolution, Image segmentation, Training, Image coding, Feature extraction BibRef

Casanova, A., Cucurull, G., Drozdzal, M., Romero, A., Bengio, Y.,
On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation,
AutoDrive18(1091-109109)
IEEE DOI 1812
Semantics, Spatial resolution, Computer architecture, Convolutional codes, Computational modeling, Image segmentation BibRef

Huang, Y., Jia, X., Georgoulis, S., Tuytelaars, T., Van Gool, L.J.,
Error Correction for Dense Semantic Image Labeling,
AutoDrive18(1111-11118)
IEEE DOI 1812
Image segmentation, Labeling, Task analysis, Semantics, Error correction, Pipelines, Probability distribution BibRef

Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.,
DenseASPP for Semantic Segmentation in Street Scenes,
CVPR18(3684-3692)
IEEE DOI 1812
Convolution, Semantics, Image resolution, Kernel, Image segmentation, Neurons, Autonomous vehicles BibRef

Matsuzuki, D.[Daisuke], Hotta, K.[Kazuhiro],
Semantic Segmentation by Integrating Classifiers for Different Difficulty Levels,
ISVC18(607-615).
Springer DOI 1811
BibRef

Ke, T.W.[Tsung-Wei], Hwang, J.J.[Jyh-Jing], Liu, Z.[Ziwei], Yu, S.X.[Stella X.],
Adaptive Affinity Fields for Semantic Segmentation,
ECCV18(I: 605-621).
Springer DOI 1810
BibRef

Zhao, H.S.[Heng-Shuang], Qi, X.J.[Xiao-Juan], Shen, X.Y.[Xiao-Yong], Shi, J.P.[Jian-Ping], Jia, J.Y.[Jia-Ya],
ICNet for Real-Time Semantic Segmentation on High-Resolution Images,
ECCV18(III: 418-434).
Springer DOI 1810
BibRef

Lin, D.[Di], Ji, Y.[Yuanfeng], Lischinski, D.[Dani], Cohen-Or, D.[Daniel], Huang, H.[Hui],
Multi-scale Context Intertwining for Semantic Segmentation,
ECCV18(III: 622-638).
Springer DOI 1810
BibRef

Zhu, X.G.[Xin-Ge], Zhou, H.[Hui], Yang, C.Y.[Ce-Yuan], Shi, J.P.[Jian-Ping], Lin, D.[Dahua],
Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation,
ECCV18(VII: 587-603).
Springer DOI 1810
BibRef

Zhang, Z.Y.[Zhen-Yu], Cui, Z.[Zhen], Xu, C.Y.[Chun-Yan], Jie, Z.Q.[Ze-Qun], Li, X.[Xiang], Yang, J.[Jian],
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation,
ECCV18(X: 238-255).
Springer DOI 1810
BibRef

Mehta, S.[Sachin], Rastegari, M.[Mohammad], Caspi, A.[Anat], Shapiro, L.[Linda], Hajishirzi, H.[Hannaneh],
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation,
ECCV18(X: 561-580).
Springer DOI 1810
BibRef

Zhang, Z.L.[Zhen-Li], Zhang, X.Y.[Xiang-Yu], Peng, C.[Chao], Xue, X.Y.[Xiang-Yang], Sun, J.[Jian],
ExFuse: Enhancing Feature Fusion for Semantic Segmentation,
ECCV18(X: 273-288).
Springer DOI 1810
BibRef

Sulimowicz, L., Ahmad, I., Aved, A.,
Superpixel-Enhanced Pairwise Conditional Random Field for Semantic Segmentation,
ICIP18(271-275)
IEEE DOI 1809
Image segmentation, Semantics, Kernel, Labeling, Robustness, Visualization, Image color analysis, and Higher-order CRFs BibRef

Lu, L.H.[Li-Hsien], Hsu, C.T.[Chiou-Ting],
Semantic Segmentation for Real-World Data by Jointly Exploiting Supervised andxs Transferrable Knowledge,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Chaurasia, A., Culurciello, E.,
LinkNet: Exploiting encoder representations for efficient semantic segmentation,
VCIP17(1-4)
IEEE DOI 1804
image resolution, image segmentation, learning (artificial intelligence), neural nets, LinkNet, Training 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

Cui, Z., Zhang, Q., Geng, S., Niu, X., Yang, J., Qiao, Y.,
Semantic segmentation with multi-path refinement and pyramid pooling dilated-resnet,
ICIP17(3100-3104)
IEEE DOI 1803
Computer architecture, Convolution, Feature extraction, Image segmentation, Semantics, Task analysis, Training, Segmentation BibRef

Zhu, Y., Tian, Y., Metaxas, D., Dollár, P.,
Semantic Amodal Segmentation,
CVPR17(3001-3009)
IEEE DOI 1711
Image edge detection, Image segmentation, Object detection, Semantics, Tools, Visualization BibRef

Shen, F., Gan, R., Yan, S., Zeng, G.,
Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF,
CVPR17(5178-5186)
IEEE DOI 1711
Complexity theory, Image segmentation, Message passing, Predictive models, Semantics, 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

Wigness, M., Rogers, J.G.,
Unsupervised Semantic Scene Labeling for Streaming Data,
CVPR17(5910-5919)
IEEE DOI 1711
Adaptation models, Data models, Image segmentation, Labeling, Semantics, Streaming media, Visualization BibRef

Bulò, S.R., Neuhold, G., Kontschieder, P.,
Loss Max-Pooling for Semantic Image Segmentation,
CVPR17(7082-7091)
IEEE DOI 1711
Benchmark testing, Image segmentation, Semantics, Standards, Training, Upper, bound BibRef

He, Y.[Yang], Keuper, M.[Margret], Schiele, B.[Bernt], Fritz, M.[Mario],
Learning Dilation Factors for Semantic Segmentation of Street Scenes,
GCPR17(41-51).
Springer DOI 1711
BibRef

Jégou, S.[Simon], Drozdzal, M.[Michal], Vazquez, D.[David], Romero, A.[Adriana], Bengio, Y.[Yoshua],
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation,
CVVT17(1175-1183)
IEEE DOI 1709
Benchmark testing, Computer architecture, Image segmentation, Semantics, Spatial resolution, Standards BibRef

Derue, F.X.[François-Xavier], Dahmane, M.[Mohamed], Lalonde, M.[Marc], Foucher, S.[Samuel],
Exploiting Semantic Segmentation for Robust Camera Motion Classification,
ICIAR17(173-181).
Springer DOI 1706
BibRef

Huang, Q.[Qin], Xia, C.Y.[Chun-Yang], Zheng, W.[Wenchao], Song, Y.H.[Yu-Hang], Xu, H.[Hao], Kuo, C.C.J.[C.C. Jay],
Object Boundary Guided Semantic Segmentation,
ACCV16(I: 197-212).
Springer DOI 1704
BibRef

Hazirbas, C.[Caner], Ma, L.N.[Ling-Ni], Domokos, C.[Csaba], Cremers, D.[Daniel],
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture,
ACCV16(I: 213-228).
Springer DOI 1704
BibRef

Bouachir, W., Torabi, A., Bilodeau, G.A., Blais, P.,
A bag of words approach for semantic segmentation of monitored scenes,
ISIVC16(88-93)
IEEE DOI 1704
Bayes methods BibRef

Souly, N.[Nasim], Shah, M.[Mubarak],
Scene Labeling Using Sparse Precision Matrix,
CVPR16(3650-3658)
IEEE DOI 1612
BibRef

Xu, C., Corso, J.J.[Jason J.],
Actor-Action Semantic Segmentation with Grouping Process Models,
CVPR16(3083-3092)
IEEE DOI 1612
BibRef

Gao, Y.[Yang], Beijbom, O.[Oscar], Zhang, N.[Ning], Darrell, T.J.[Trevor J.],
Compact Bilinear Pooling,
CVPR16(317-326)
IEEE DOI 1612
BibRef

Bearman, A.[Amy], Russakovsky, O.[Olga], Ferrari, V.[Vittorio], Fei-Fei, L.[Li],
What's the Point: Semantic Segmentation with Point Supervision,
ECCV16(VII: 549-565).
Springer DOI 1611
BibRef

Ghiasi, G.[Golnaz], Fowlkes, C.C.[Charless C.],
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation,
ECCV16(III: 519-534).
Springer DOI 1611
BibRef

Fourure, D.[Damien], Emonet, R.[Rémi], Fromont, E.[Elisa], Muselet, D.[Damien], Trémeau, A.[Alain], Wolf, C.[Christian],
Semantic Segmentation via Multi-task, Multi-domain Learning,
SSSPR16(333-343).
Springer DOI 1611
BibRef

Tarashima, S., Pan, J., Irie, G., Kurozumi, T., Kinebuchi, T.,
Joint object discovery and segmentation with image-wise reconstruction error,
ICIP16(849-853)
IEEE DOI 1610
Airplanes BibRef

Zhou, H., Zhang, J.[Jun], Lei, J.[Jun], Li, S.[Shuohao], Tu, D.[Dan],
Image semantic segmentation based on FCN-CRF model,
ICIVC16(9-14)
IEEE DOI 1610
feature extraction BibRef

Li, W.H.[Wei-Hao], Yang, M.Y.[Michael Ying],
Efficient Semantic Segmentation Of Man-made Scenes Using Fully-connected Conditional Random Field,
ISPRS16(B3: 633-640).
DOI Link 1610
BibRef

Tian, Q., Li, B.,
Simultaneous semantic segmentation of a set of partially labeled images,
WACV16(1-9)
IEEE DOI 1606
Computer science BibRef

Najafi, M.[Mohammad], Namin, S.T.[Sarah Taghavi], Salzmann, M.[Mathieu], Petersson, L.[Lars],
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering,
CVPR16(607-615)
IEEE DOI 1612
BibRef
Earlier: A2, A1, A3, A4:
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing,
ICCV15(1188-1196)
IEEE DOI 1602
Feature extraction. combine modalities. BibRef

Qi, X.J.[Xiao-Juan], Liu, Z.Z.[Zheng-Zhe], Shi, J.P.[Jian-Ping], Zhao, H.S.[Heng-Shuang], Jia, J.Y.[Jia-Ya],
Augmented Feedback in Semantic Segmentation Under Image Level Supervision,
ECCV16(VIII: 90-105).
Springer DOI 1611
BibRef

Qi, X.J.[Xiao-Juan], Shi, J.P.[Jian-Ping], Liu, S., Liao, R., Jia, J.Y.[Jia-Ya],
Semantic Segmentation with Object Clique Potential,
ICCV15(2587-2595)
IEEE DOI 1602
Computational modeling BibRef

Caesar, H.[Holger], Uijlings, J.[Jasper], Ferrari, V.[Vittorio],
Region-Based Semantic Segmentation with End-to-End Training,
ECCV16(I: 381-397).
Springer DOI 1611
BibRef
Earlier:
Joint Calibration for Semantic Segmentation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Srivatsa, R.S.[R. Sai], Babu, R.V.[R. Venkatesh],
Salient object detection via objectness measure,
ICIP15(4481-4485)
IEEE DOI 1512
Image Saliency; Image Segmentation; Objectness Proposals; Superpixels BibRef

Ventura, C.[Carles], Giro-i-Nieto, X.[Xavier], Vilaplana, V.[Veronica], McGuinness, K.[Kevin], Marques, F.[Ferran], O'Connor, N.E.[Noel E.],
Improving spatial codification in semantic segmentation,
ICIP15(3605-3609)
IEEE DOI 1512
Object recognition BibRef

Zhao, N.[Nan], Banerjee, C.[Chaity], Liu, X.W.[Xiu-Wen],
Nano-scale context-sensitive semantic segmentation,
ICIP15(3062-3066)
IEEE DOI 1512
Nano-scale; context-sensitive; microvilli; semantic segmentation; spike BibRef

Pieck, M.A.R.[Martin A.R.], van der Sommen, F.[Fons], Zinger, S.[Svitlana], de With, P.H.N.[Peter H.N.],
Real-time semantic context labeling for image understanding,
ICIP15(3180-3184)
IEEE DOI 1512
Context classification; Gabor filtering; SVM; Segmentation BibRef

Dai, J.F.[Ji-Feng], He, K.M.[Kai-Ming], Sun, J.[Jian],
Convolutional feature masking for joint object and stuff segmentation,
CVPR15(3992-4000)
IEEE DOI 1510
BibRef

Mostajabi, M.[Mohammadreza], Yadollahpour, P.[Payman], Shakhnarovich, G.[Gregory],
Feedforward semantic segmentation with zoom-out features,
CVPR15(3376-3385)
IEEE DOI 1510
BibRef

Ardeshir, S.[Shervin], Collins-Sibley, K.M.[Kofi Malcolm], Shah, M.[Mubarak],
Geo-semantic segmentation,
CVPR15(2792-2799)
IEEE DOI 1510
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

Zhu, G.[Gao], Ming, Y.S.[Yan-Sheng], Li, H.D.[Hong-Dong],
Object category detection by incorporating mid-level grouping cues,
ICIP14(1604-1608)
IEEE DOI 1502
Computer vision BibRef

Bassiouny, A.[Ahmed], El-Saban, M.[Motaz],
Semantic segmentation as image representation for scene recognition,
ICIP14(981-985)
IEEE DOI 1502
Accuracy BibRef

Tegen, A.[Agnes], Weegar, R.[Rebecka], Hammarlund, L.[Linus], Oskarsson, M.[Magnus], Jiang, F.Y.[Fang-Yuan], Medved, D.[Dennis], Nugues, P.[Pierre], Astrom, K.[Kalle],
Image Segmentation and Labeling Using Free-Form Semantic Annotation,
ICPR14(2281-2286)
IEEE DOI 1412
Context BibRef

Kroeger, T.[Thorben], Kappes, J.H.[Jörg H.], Beier, T.[Thorsten], Koethe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Asymmetric Cuts: Joint Image Labeling and Partitioning,
GCPR14(199-211).
Springer DOI 1411
BibRef

Ladicky, L.[Lubor], Shi, J.B.[Jian-Bo], Pollefeys, M.[Marc],
Pulling Things out of Perspective,
CVPR14(89-96)
IEEE DOI 1409
Depth Estimation; Object Recognition; Semantic Segmentation BibRef

Mottaghi, R.[Roozbeh], Chen, X.J.[Xian-Jie], Liu, X.B.[Xiao-Bai], Cho, N.G.[Nam-Gyu], Lee, S.W.[Seong-Whan], Fidler, S.[Sanja], Urtasun, R.[Raquel], Yuille, A.L.[Alan L.],
The Role of Context for Object Detection and Semantic Segmentation in the Wild,
CVPR14(891-898)
IEEE DOI 1409
BibRef

Isola, P.[Phillip], Zoran, D.[Daniel], Krishnan, D.[Dilip], Adelson, E.H.[Edward H.],
Crisp Boundary Detection Using Pointwise Mutual Information,
ECCV14(III: 799-814).
Springer DOI 1408
Between semantic objects. BibRef

Li, Z.Y.[Zhen-Yang], Gavves, E.[Efstratios], Mensink, T.[Thomas], Snoek, C.G.M.[Cees G.M.],
Attributes Make Sense on Segmented Objects,
ECCV14(VI: 350-365).
Springer DOI 1408
BibRef

Dong, J.[Jian], Chen, Q.A.[Qi-Ang], Yan, S.C.[Shui-Cheng], Yuille, A.L.[Alan L.],
Towards Unified Object Detection and Semantic Segmentation,
ECCV14(V: 299-314).
Springer DOI 1408
Joint detect and segment. BibRef

Riemenschneider, H.[Hayko], Bódis-Szomorú, A.[András], Weissenberg, J.[Julien], Van Gool, L.J.[Luc J.],
Learning Where to Classify in Multi-view Semantic Segmentation,
ECCV14(V: 516-532).
Springer DOI 1408
BibRef

Tao, L.L.[Ling-Ling], Porikli, F.M.[Fatih M.], Vidal, R.[René],
Sparse Dictionaries for Semantic Segmentation,
ECCV14(V: 549-564).
Springer DOI 1408
BibRef

Zhu, S.Q.[Sheng-Qi], Yang, Y.Q.[Yi-Qing], Zhang, L.[Li],
From Label Maps to Label Strokes: Semantic Segmentation for Street Scenes from Incomplete Training Data,
CVCP13(468-475)
IEEE DOI 1403
data handling BibRef

Baek, S.[Seung_Ryul], Lim, T.[Taegyu], Heo, Y.S.[Yong Seok], Park, S.B.[Sung-Bum], Kwak, H.[Hantak], Shim, W.[Woosung],
Superpixel Coherency and Uncertainty Models for Semantic Segmentation,
PGMs13(275-282)
IEEE DOI 1403
computational complexity BibRef

Roig, G.[Gemma], Boix, X.[Xavier], de Nijs, R.[Roderick], Ramos, S.[Sebastian], Kuhnlenz, K.[Koljia], Van Gool, L.J.[Luc J.],
Active MAP Inference in CRFs for Efficient Semantic Segmentation,
ICCV13(2312-2319)
IEEE DOI 1403
using expensive features. BibRef

Barron, J.T.[Jonathan T.], Biggin, M.D.[Mark D.], Arbelaez, P.[Pablo], Knowles, D.W.[David W.], Keranen, S.V.E.[Soile V.E.], Malik, J.[Jitendra],
Volumetric Semantic Segmentation Using Pyramid Context Features,
ICCV13(3448-3455)
IEEE DOI 1403
BibRef

Singh, G.[Gautam], Kosecka, J.[Jana],
Introspective semantic segmentation,
WACV14(714-720)
IEEE DOI 1406
BibRef
Earlier:
Nonparametric Scene Parsing with Adaptive Feature Relevance and Semantic Context,
CVPR13(3151-3157)
IEEE DOI 1309
feature relevance; scene understanding; semantic segmentation. Small patches, simple features. Accuracy. BibRef

Csurka, G.[Gabriela], Larlus, D.[Diane], Perronnin, F.[Florent],
What is a good evaluation measure for semantic segmentation?,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Li, F.X.[Fu-Xin], Carreira, J.[Joao], Lebanon, G.[Guy], Sminchisescu, C.[Cristian],
Composite Statistical Inference for Semantic Segmentation,
CVPR13(3302-3309)
IEEE DOI 1309
composite likelihood BibRef

Zou, W.B.[Wen-Bin], Kpalma, K.[Kidiyo], Ronsin, J.[Joseph],
Semantic image segmentation using region bank,
ICPR12(922-925).
WWW Link. 1302
BibRef
And:
Semantic segmentation via sparse coding over hierarchical regions,
ICIP12(2577-2580).
IEEE DOI 1302
Hierarchical region segmentation, sparse coding of regions for recognition. BibRef

Fröhlich, B.[Björn], Rodner, E.[Erik], Denzler, J.[Joachim],
Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach,
ACCV12(I:218-231).
Springer DOI 1304
BibRef
And:
As Time Goes by: Anytime Semantic Segmentation with Iterative Context Forests,
DAGM12(1-10).
Springer DOI 1209
BibRef

Hariharan, B.[Bharath], Zitnick, C.L.[C. Lawrence], Dollar, P.[Piotr],
Detecting Objects Using Deformation Dictionaries,
CVPR14(1995-2002)
IEEE DOI 1409
BibRef

Fidler, S.[Sanja], Mottaghi, R.[Roozbeh], Yuille, A.L.[Alan L.], Urtasun, R.[Raquel],
Bottom-Up Segmentation for Top-Down Detection,
CVPR13(3294-3301)
IEEE DOI 1309
Object detection; object class recognition; object segmentation BibRef

Yao, J.[Jian], Fidler, S.[Sanja], Urtasun, R.[Raquel],
Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation,
CVPR12(702-709).
IEEE DOI 1208
BibRef

Pohlen, T., Badami, I., Mathias, M., Leibe, B.[Bastian],
Semantic segmentation of modular furniture,
WACV16(1-9)
IEEE DOI 1606
Face BibRef

Floros, G.[Georgios], Rematas, K.[Konstantinos], Leibe, B.[Bastian],
Multi-Class Image Labeling with Top-Down Segmentation and Generalized Robust P^N Potentials,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Passino, G.[Giuseppe], Patras, I.[Ioannis], Izquierdo, E.[Ebroul],
Pyramidal Model for Image Semantic Segmentation,
ICPR10(1554-1557).
IEEE DOI 1008
BibRef

Schnitman, Y.[Yaar], Caspi, Y.[Yaron], Cohen-Or, D.[Daniel], Lischinski, D.[Dani],
Inducing Semantic Segmentation from an Example,
ACCV06(II:373-384).
Springer DOI 0601
BibRef

Zhang, H.H.[Hong-Hui], Xiao, J.X.[Jian-Xiong], Quan, L.[Long],
Supervised Label Transfer for Semantic Segmentation of Street Scenes,
ECCV10(V: 561-574).
Springer DOI 1009
Set of labelled images of street scenes. Recognition is by matching at image level, then using the given lables. BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Domain Adaption for Semantic Segmentation .