8.6.3 Semantic Segmentation, Label and Segment Together

Chapter Contents (Back)
Semantic Segmentation.
See also Remote Sensing Semantic Segmentation.
See also Neural Networks for Segmentation.
See also Neural Networks for Semantic Segmentation.
See also Video Semantic Object Segmentation.

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

Korner, M.[Marco], Krishna, M.V.[Mahesh V.], Susse, H.[Herbert], Ortmann, W.[Wolfang], Denzler, J.[Joachim],
Regularized Geometric Hulls for Bio-medical Image Segmentation,
BMVA(2015), No. 4, 2015, pp. 1-12.
PDF File. 1509
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

Ravě, 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

Li, Y., Guo, Y., Kao, Y., He, R.,
Image Piece Learning for Weakly Supervised Semantic Segmentation,
SMCS(47), No. 4, April 2017, pp. 648-659.
IEEE DOI 1704
Correlation 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.[Yakun], 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

Shimoda, W.[Wataru], Yanai, K.[Keiji],
Weakly supervised semantic segmentation using distinct class specific saliency maps,
CVIU(191), 2020, pp. 102712.
Elsevier DOI 2002
BibRef
Earlier:
Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic Segmentation,
ECCV16(IV: 218-234).
Springer DOI 1611
Semantic segmentation, Weakly supervised learning, Weakly supervised segmentation, Visualization, Deep learning BibRef

Shimoda, W.[Wataru], Yanai, K.[Keiji],
Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation,
ICCV19(5207-5216)
IEEE DOI 2004
estimation theory, image denoising, image segmentation, iterative methods, learning (artificial intelligence), Predictive models 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

Wang, X.[Xiang], Liu, S.F.[Si-Fei], Ma, H.M.[Hui-Min], Yang, M.H.[Ming-Hsuan],
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning,
IJCV(128), No. 6, June 2020, pp. 1736-1749.
Springer DOI 2006
BibRef

Chen, Y.C.[Yun-Chun], Lin, Y.Y.[Yen-Yu], Yang, M.H.[Ming-Hsuan], Huang, J.B.[Jia-Bin],
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation,
PAMI(43), No. 10, October 2021, pp. 3632-3647.
IEEE DOI 2109
Semantics, Task analysis, Image segmentation, Training, Clutter, Proposals, Pattern matching, Semantic matching, weakly-supervised learning BibRef

Li, R.[Rui], Cao, W.M.[Wen-Ming], Jiao, Q.[Qianfen], Wu, S.[Si], Wong, H.S.[Hau-San],
Simplified unsupervised image translation for semantic segmentation adaptation,
PR(105), 2020, pp. 107343.
Elsevier DOI 2006
Domain adaptation, Image segmentation, Image translation 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, Computer vision BibRef

Vu, T., Jain, H., Bucher, M., Cord, M., Pérez, P.P.,
DADA: Depth-Aware Domain Adaptation in Semantic Segmentation,
ICCV19(7363-7372)
IEEE DOI 2004
data models, image segmentation, DADA, depth-aware domain adaptation, unsupervised domain adaptation, Benchmark testing BibRef

Seed picking crossover optimisation algorithm for semantic segmentation from images,
IET-IPR(14), No. 11, September 2020, pp. 2503-2511.
DOI Link 2009
No Authors Listed BibRef

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, Computer vision, 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
BibRef

Zhang, Y.[Yifei], 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

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

Chan, L.[Lyndon], Hosseini, M.S.[Mahdi S.], Plataniotis, K.N.[Konstantinos N.],
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains,
IJCV(129), No. 2, February 2021, pp. 361-384.
Springer DOI 2102
BibRef

Huang, L.[Li], He, M.L.[Mei-Ling], Tan, C.[Chong], Jiang, D.[Du], Li, G.F.[Gong-Fa], Yu, H.[Hui],
Jointly network image processing: multi-task image semantic segmentation of indoor scene based on CNN,
IET-IPR(14), No. 15, 15 December 2020, pp. 3689-3697.
DOI Link 2103
BibRef

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

Li, G.R.[Guang-Rui], Kang, G.L.[Guo-Liang], Liu, W.[Wu], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi],
Content-consistent Matching for Domain Adaptive Semantic Segmentation,
ECCV20(XIV:440-456).
Springer DOI 2011
BibRef

Xu, H.Q.[Han-Qing], Yang, M.[Ming], Deng, L.Y.[Liu-Yuan], Qian, Y.Q.[Ye-Qiang], Wang, C.X.[Chun-Xiang],
Neutral Cross-Entropy Loss Based Unsupervised Domain Adaptation for Semantic Segmentation,
IP(30), 2021, pp. 4516-4525.
IEEE DOI 2105
Entropy, Semantics, Image segmentation, Minimization, Training, Perturbation methods, Optimization, Semantic segmentation, gradient neutralization BibRef

Ji, J.[Jian], Shi, R.[Rui], Li, S.[Sitong], Chen, P.[Peng], Miao, Q.[Qiguang],
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

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

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

Krešo, I.[Ivan], Krapac, J.[Josip], Šegvic, S.[Siniša],
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images,
ITS(22), No. 8, August 2021, pp. 4951-4961.
IEEE DOI 2108
BibRef
Earlier: A2, A1, A3:
Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images,
CVRoads17(238-245)
IEEE DOI 1802
BibRef
Earlier: A2, A3, Only:
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels,
GCPR16(377-388).
Springer DOI 1611
Semantics, Feature extraction, Image segmentation, Computational modeling, Spatial resolution, Checkpointing, road transportation. Convolution, Tensile stress, Training BibRef

Krešo, I.[Ivan], Cauševic, D.[Denis], Krapac, J.[Josip], Šegvic, S.[Siniša],
Convolutional Scale Invariance for Semantic Segmentation,
GCPR16(64-75).
Springer DOI 1611
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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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
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Hao, X.C.[Xiao-Chen], Hao, X.[Xingjun], 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


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

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

Yang, J.Y.[Jin-Yu], An, W.Z.[Wei-Zhi], Yan, C.C.[Chao-Chao], Zhao, P.L.[Pei-Lin], Huang, J.Z.[Jun-Zhou],
Context-Aware Domain Adaptation in Semantic Segmentation,
WACV21(514-524)
IEEE DOI 2106
Adaptation models, Aggregates, Semantics BibRef

Yin, J.H.[Jun-Hui], Zhang, S.Q.[Si-Qing], Chang, D.L.[Dong-Liang], Ma, Z.Y.[Zhan-Yu], Guo, J.[Jun],
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization,
ICPR21(4229-4236)
IEEE DOI 2105
weakly supervised object localization. Location awareness, Training, Deep learning, Adaptation models, Visualization, Automobiles 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

Rosas-Arias, L.[Leonel], Benitez-Garcia, G.[Gibran], Portillo-Portillo, J.[José], Sánchez-Pérez, G.[Gabriel], Yanai, K.[Keiji],
Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions,
ICPR21(2264-2271)
IEEE DOI 2105
Convolutional codes, Image resolution, Quantization (signal), Semantics, Real-time systems, Decoding, Pattern recognition 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.[Yifei], 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
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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
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Mozaffari, M.H.[M. Hamed], Lee, W.S.[Won-Sook],
Semantic Segmentation with Peripheral Vision,
ISVC20(II:421-429).
Springer DOI 2103
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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
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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).
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Zhang, T.Y.[Tian-Yi], Lin, G.S.[Guo-Sheng], Liu, W.D.[Wei-De], Cai, J.F.[Jian-Fei], Kot, A.[Alex],
Splitting Vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation,
ECCV20(XXII:663-679).
Springer DOI 2011
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Fan, J.S.[Jun-Song], Zhang, Z.X.[Zhao-Xiang], Tan, T.N.[Tie-Niu],
Employing Multi-estimations for Weakly-supervised Semantic Segmentation,
ECCV20(XVII:332-348).
Springer DOI 2011
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Chen, W.L.[Wan-Li], Zhu, X.G.[Xin-Ge], Sun, R.[Ruoqi], He, J.[Junjun], Li, R.[Ruiyu], Shen, X.Y.[Xiao-Yong], Yu, B.[Bei],
Tensor Low-rank Reconstruction for Semantic Segmentation,
ECCV20(XVII:52-69).
Springer DOI 2011
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Hu, H.Z.[Han-Zhe], Ji, D.[Deyi], 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
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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.
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Huang, J.X.[Jia-Xing], Lu, S.[Shijian], Guan, D.[Dayan], Zhang, X.B.[Xiao-Bing],
Contextual-relation Consistent Domain Adaptation for Semantic Segmentation,
ECCV20(XV:705-722).
Springer DOI 2011
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Subhani, M.N.[M. Naseer], Ali, M.[Mohsen],
Learning from Scale-invariant Examples for Domain Adaptation in Semantic Segmentation,
ECCV20(XXII:290-306).
Springer DOI 2011
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Yang, J.[Jinyu], An, W.Z.[Wei-Zhi], Wang, S.[Sheng], Zhu, X.L.[Xin-Liang], Yan, C.C.[Chao-Chao], Huang, J.Z.[Jun-Zhou],
Label-driven Reconstruction for Domain Adaptation in Semantic Segmentation,
ECCV20(XXVII:480-498).
Springer DOI 2011
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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

Chen, L.[Liyi], Wu, W.W.[Wei-Wei], Fu, C.C.[Chen-Chen], Han, X.[Xiao], Zhang, Y.T.[Yun-Tao],
Weakly Supervised Semantic Segmentation with Boundary Exploration,
ECCV20(XXVI:347-362).
Springer DOI 2011
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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
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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
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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
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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
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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
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Kamann, C.[Christoph], Rother, C.[Carsten],
Increasing the Robustness of Semantic Segmentation Models with Painting-by-numbers,
ECCV20(X:369-387).
Springer DOI 2011
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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
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Paul, S.[Sujoy], Tsai, Y.H.[Yi-Hsuan], Schulter, S.[Samuel], Roy-Chowdhury, A.K.[Amit K.], Chandraker, M.[Manmohan],
Domain Adaptive Semantic Segmentation Using Weak Labels,
ECCV20(IX:571-587).
Springer DOI 2011
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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
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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
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Sun, G.L.[Guo-Lei], Wang, W.G.[Wen-Guan], Dai, J.F.[Ji-Feng], Van Gool, L.J.[Luc J.],
Mining Cross-image Semantics for Weakly Supervised Semantic Segmentation,
ECCV20(II:347-365).
Springer DOI 2011
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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
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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
Computer vision, 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

Chang, Y., Wang, Q., Hung, W., Piramuthu, R., Tsai, Y., Yang, M.,
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration,
CVPR20(8988-8997)
IEEE DOI 2008
Task analysis, Feature extraction, Semantics, Training, Image segmentation, Computational modeling, Computer vision 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

Wang, Y.[Yude], Zhang, J.[Jie], Kan, M.[Meina], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation,
CVPR20(12272-12281)
IEEE DOI 2008
Image segmentation, Semantics, Phase change materials, Task analysis, Correlation, Aggregates, Supervised learning BibRef

Liu, X., Ji, W., You, J., El Fakhri, G., Woo, J.,
Severity-Aware Semantic Segmentation With Reinforced Wasserstein Training,
CVPR20(12563-12572)
IEEE DOI 2008
Semantics, Autonomous vehicles, Measurement, Automobiles, Histograms, Training, Roads 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

Fan, J., Zhang, Z., Song, C., Tan, T.,
Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation,
CVPR20(4282-4291)
IEEE DOI 2008
Image segmentation, Semantics, Training, Task analysis, Manifolds, Estimation, Benchmark testing BibRef

Pan, F., Shin, I., Rameau, F., Lee, S., Kweon, I.S.,
Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision,
CVPR20(3763-3772)
IEEE DOI 2008
Adaptation models, Entropy, Image segmentation, Semantics, Generators, Data models, Task analysis 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

Yang, Y., Soatto, S.,
FDA: Fourier Domain Adaptation for Semantic Segmentation,
CVPR20(4084-4094)
IEEE DOI 2008
Semantics, Image segmentation, Training, Entropy, Adaptation models, Task analysis, Frequency-domain analysis 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

Zareian, A., Karaman, S., Chang, S.,
Weakly Supervised Visual Semantic Parsing,
CVPR20(3733-3742)
IEEE DOI 2008
Semantics, Visualization, Proposals, Image edge detection, Message passing, Task analysis, Computer vision BibRef

Wang, Z., Wei, Y., Feris, R., Xiong, J., Hwu, W., Huang, T.S., Shi, H.,
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation,
VL3W20(4043-4047)
IEEE DOI 2008
Semantics, Task analysis, Adaptation models, Image segmentation, Training, Feature extraction, Urban areas 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

Stekovic, S., Fraundorfer, F., Lepetit, V.,
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

Sakaridis, C.[Christos], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation,
ICCV19(7373-7382)
IEEE DOI 2004
image annotation, image segmentation, learning (artificial intelligence), Uncertainty 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

Yu, Z., Zhuge, Y., Lu, H., Zhang, L.,
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation,
ICCV19(7222-7232)
IEEE DOI 2004
image classification, image coding, image recognition, image segmentation, object detection, supervised learning, WSSS, Computational modeling 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.,
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

Luo, Y., Liu, P., Guan, T., Yu, J., Yang, Y.,
Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation,
ICCV19(6777-6786)
IEEE DOI 2004
feature extraction, image classification, image segmentation, neural nets, unsupervised learning, Data mining BibRef

Han, Q.Y.[Qiu-Yuan], Zheng, J.[Jin],
Multi-scale Spatial Location Preference for Semantic Segmentation,
MMMod20(I:593-604).
Springer DOI 2003
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Shen, Y.H.[Yun-Hang], Ji, R.R.[Rong-Rong], Wang, Y.[Yan], Wu, Y.J.[Yong-Jian], Cao, L.J.[Liu-Juan],
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation,
CVPR19(697-707).
IEEE DOI 2002
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Jiao, J.B.[Jian-Bo], Wei, Y.[Yunchao], Jie, Z.[Zequn], 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
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Liu, Y.[Yifan], Chen, K.[Ke], Liu, C.[Chris], Qin, Z.C.[Zeng-Chang], Luo, Z.[Zhenbo], Wang, J.D.[Jing-Dong],
Structured Knowledge Distillation for Semantic Segmentation,
CVPR19(2599-2608).
IEEE DOI 2002
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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
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Song, C.F.[Chun-Feng], Huang, Y.[Yan], Ouyang, W.L.[Wan-Li], Wang, L.[Liang],
Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation,
CVPR19(3131-3140).
IEEE DOI 2002
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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
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Zhang, H.[Hang], Zhang, H.[Han], Wang, C.[Chenguang], Xie, J.[Junyuan],
Co-Occurrent Features in Semantic Segmentation,
CVPR19(548-557).
IEEE DOI 2002
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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
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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
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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
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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

Lin, Y.X.[Yong-Xiang], Tan, D.S.[Daniel Stanley], Cheng, W.H.[Wen-Huang], Chen, Y.Y.[Yung-Yao], Hua, K.L.[Kai-Lung],
Spatially-Aware Domain Adaptation for Semantic Segmentation of Urban Scenes,
ICIP19(1870-1874)
IEEE DOI 1910
Semantic segmentation, Domain adaptation, Spatial Structure 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

Falcăo, A.[Alexandre], Bragantini, J.[Jordăo],
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

Lv, F.M.[Feng-Mao], Lian, Q.[Qing], Yang, G.[Guowu], Lin, G.S.[Guo-Sheng], Pan, S.J.[Sinno Jialin], Duan, L.X.[Li-Xin],
Domain Adaptive Semantic Segmentation Through Structure Enhancement,
TASKCV18(II:172-179).
Springer DOI 1905
BibRef

Yamazaki, M.[Masaki], Peng, X.C.[Xing-Chao], Saito, K.[Kuniaki], Hu, P.[Ping], Saenko, K.[Kate], Taniguchi, Y.[Yasuhiro],
Weakly Supervised Domain Adaptation using Super-pixel labeling for Semantic Segmentation,
MVA21(1-5)
DOI Link 2109
Deep learning, Image segmentation, Adaptation models, Annotations, Semantics, Object segmentation, Data models BibRef

Watanabe, K.[Kohei], Saito, K.[Kuniaki], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation,
AutoNUE18(V:600-616).
Springer DOI 1905
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

Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.,
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation,
CVPR18(7268-7277)
IEEE DOI 1812
Image segmentation, Semantics, Convolution, Training, Kernel, Standards, Head 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.,
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, Computer vision, Image segmentation BibRef

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

Zou, Y.[Yang], Yu, Z.D.[Zhi-Ding], Kumar, B.V.K.V.[B. V. K. Vijaya], Wang, J.S.[Jin-Song],
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training,
ECCV18(III: 297-313).
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
computer vision, 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

Vernaza, P., Chandraker, M.,
Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation,
CVPR17(2953-2961)
IEEE DOI 1711
Image edge detection, Image segmentation, Labeling, Semantics, Training, Uncertainty 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

Bulň, 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.[Lingni], 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

Xing, F.Z., Cambria, E., Huang, W.B., Xu, Y.,
Weakly supervised semantic segmentation with superpixel embedding,
ICIP16(1269-1273)
IEEE DOI 1610
Context 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.[Weihao], 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

Varas, D., Alfaro, M., Marques, F.,
Multiresolution Hierarchy Co-Clustering for Semantic Segmentation in Sequences with Small Variations,
ICCV15(4579-4587)
IEEE DOI 1602
Image resolution BibRef

Deng, Z., Todorovic, S., Latecki, L.J.,
Semantic Segmentation of RGBD Images with Mutex Constraints,
ICCV15(1733-1741)
IEEE DOI 1602
Computational modeling BibRef

Pourian, N., Karthikeyan, S., Manjunath, B.S.,
Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts,
ICCV15(1359-1367)
IEEE DOI 1602
Correlation 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

Zhang, W.[Wei], Zeng, S.[Sheng], Wang, D.[Dequan], Xue, X.Y.[Xiang-Yang],
Weakly supervised semantic segmentation for social images,
CVPR15(2718-2726)
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

Ying, P.[Peng], Liu, J.[Jing], Lu, H.Q.[Han-Qing],
Dictionary learning based superpixels clustering for weakly-supervised semantic segmentation,
ICIP15(4258-4262)
IEEE DOI 1512
Weak supervision;dictionary learning;semantic segmentation BibRef

Liu, Y.[Yang], Liu, J.[Jing], Li, Z.[Zechao], Tang, J.H.[Jin-Hui], Lu, H.Q.[Han-Qing],
Weakly-Supervised Dual Clustering for Image Semantic Segmentation,
CVPR13(2075-2082)
IEEE DOI 1309
Image Semantic Segmentation; Weakly-Supervised 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
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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).
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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,
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HTML Version. 1110
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Passino, G.[Giuseppe], Patras, I.[Ioannis], Izquierdo, E.[Ebroul],
Pyramidal Model for Image Semantic Segmentation,
ICPR10(1554-1557).
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Schnitman, Y.[Yaar], Caspi, Y.[Yaron], Cohen-Or, D.[Daniel], Lischinski, D.[Dani],
Inducing Semantic Segmentation from an Example,
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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
Remote Sensing Semantic Segmentation .


Last update:Sep 19, 2021 at 21:11:01