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
Jiang, J.,
Song, X.,
An Optimized Higher Order CRF for Automated Labeling and Segmentation
of Video Objects,
CirSysVideo(26), No. 3, March 2016, pp. 506-516.
IEEE DOI
1603
Color
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
Shelhamer, E.[Evan],
Long, J.[Jonathan],
Darrell, T.J.[Trevor J.],
Fully Convolutional Networks for Semantic Segmentation,
PAMI(39), No. 4, April 2017, pp. 640-651.
IEEE DOI
1703
BibRef
Earlier: A2, A1, A3:
CVPR15(3431-3440)
IEEE DOI
1510
Award, CVPR, HM. Computer architecture
BibRef
Shelhamer, E.[Evan],
Rakelly, K.[Kate],
Hoffman, J.[Judy],
Darrell, T.J.[Trevor J.],
Clockwork Convnets for Video Semantic Segmentation,
VSeg16(III: 852-868).
Springer DOI
1611
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
Wang, L.T.[Lian-Tao],
Meng, D.,
Hu, X.L.[Xue-Lei],
Lu, J.F.[Jian-Feng],
Zhao, J.[Ji],
Instance Annotation via Optimal BoW for Weakly Supervised Object
Localization,
Cyber(47), No. 5, May 2017, pp. 1313-1324.
IEEE DOI
1704
BibRef
Earlier: A1, A5, A3, A4, Only:
Weakly supervised object localization via maximal entropy randomwalk,
ICIP14(1614-1617)
IEEE DOI
1502
Birds.
Entropy
BibRef
Zheng, C.[Chen],
Zhang, Y.[Yun],
Wang, L.G.[Lei-Guang],
Semantic Segmentation of Remote Sensing Imagery Using an Object-Based
Markov Random Field Model With Auxiliary Label Fields,
GeoRS(55), No. 5, May 2017, pp. 3015-3028.
IEEE DOI
1705
Markov processes, image segmentation, remote sensing,
auxiliary label field, hierarchical semantic information,
object-based MRF model, object-based Markov random field model,
probability distribution, remote sensing imagery,
remote sensing images, semantic segmentation, Context,
Context modeling, Image segmentation, Probability distribution,
Remote sensing, Semantics, Spatial resolution,
Auxiliary label field, object-based Markov random field,
remote sensing image, semantic, segmentation
BibRef
Liu, Y.[Yu],
Nguyen, D.M.[Duc Minh],
Deligiannis, N.[Nikos],
Ding, W.[Wenrui],
Munteanu, A.[Adrian],
Hourglass-Shape Network Based Semantic Segmentation for High
Resolution Aerial Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Czúni, L.[László],
Rashad, M.[Metwally],
The use of IMUs for video object retrieval in lightweight devices,
JVCIR(48), No. 1, 2017, pp. 30-42.
Elsevier DOI
1708
BibRef
Earlier:
View centered video-based object recognition for lightweight devices,
WSSIP16(1-4)
IEEE DOI
1608
Video object retrieval.
image recognition
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
Badrinarayanan, V.[Vijay],
Kendall, A.[Alex],
Cipolla, R.[Roberto],
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
Segmentation,
PAMI(39), No. 12, December 2017, pp. 2481-2495.
IEEE DOI
1711
Convolutional codes, Decoding,
Image segmentation, Neural networks, Semantics, Training,
indoor scenes, pooling, road scenes,
semantic pixel-wise segmentation, upsampling
BibRef
Cipolla, R.[Roberto],
Gal, Y.,
Kendall, A.[Alex],
Multi-task Learning Using Uncertainty to Weigh Losses for Scene
Geometry and Semantics,
CVPR18(7482-7491)
IEEE DOI
1812
Task analysis, Uncertainty, Semantics, Geometry, Image segmentation,
Computational modeling
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
Arnab, A.[Anurag],
Zheng, S.[Shuai],
Jayasumana, S.[Sadeep],
Romera-Paredes, B.[Bernardino],
Larsson, M.,
Kirillov, A.,
Savchynskyy, B.,
Rother, C.,
Kahl, F.,
Torr, P.H.S.[Philip H. S.],
Conditional Random Fields Meet Deep Neural Networks for Semantic
Segmentation: Combining Probabilistic Graphical Models with Deep
Learning for Structured Prediction,
SPMag(35), No. 1, January 2018, pp. 37-52.
IEEE DOI
1801
Computational modeling, Computer vision, Feature extraction,
Image segmentation, Semantics, Visualization
BibRef
Arnab, A.[Anurag],
Jayasumana, S.[Sadeep],
Zheng, S.[Shuai],
Torr, P.H.S.[Philip H. S.],
Higher Order Conditional Random Fields in Deep Neural Networks,
ECCV16(II: 524-540).
Springer DOI
1611
BibRef
Arnab, A.[Anurag],
Torr, P.H.S.[Philip H. S.],
Pixelwise Instance Segmentation with a Dynamically Instantiated
Network,
CVPR17(879-888)
IEEE DOI
1711
Detectors, Image segmentation, Object detection, Pipelines,
Proposals, Semantics. First, semantic segmentation, then object instance
detection.
BibRef
Arnab, A.[Anurag],
Torr, P.H.S.[Philip H.S.],
Bottom-up Instance Segmentation using Deep Higher-Order CRFs,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Zheng, S.[Shuai],
Jayasumana, S.[Sadeep],
Romera-Paredes, B.[Bernardino],
Vineet, V.[Vibhav],
Su, Z.Z.[Zhi-Zhong],
Du, D.L.[Da-Long],
Huang, C.[Chang],
Torr, P.H.S.[Philip H. S.],
Conditional Random Fields as Recurrent Neural Networks,
ICCV15(1529-1537)
IEEE DOI
1602
Combine CNN with CRF.
BibRef
Larsson, M.[Mĺns],
Alvén, J.[Jennifer],
Kahl, F.[Fredrik],
Max-Margin Learning of Deep Structured Models for Semantic Segmentation,
SCIA17(II: 28-40).
Springer DOI
1706
BibRef
Saleh, F.S.[Fatemehsadat S.],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Gould, S.[Stephen],
Incorporating Network Built-in Priors in Weakly-Supervised Semantic
Segmentation,
PAMI(40), No. 6, June 2018, pp. 1382-1396.
IEEE DOI
1805
BibRef
Earlier: A1, A2, A3, A4, A6, A5:
Built-in Foreground/Background Prior for Weakly-Supervised Semantic
Segmentation,
ECCV16(VIII: 413-432).
Springer DOI
1611
Data mining, Image segmentation, Machine learning, Neural networks,
Object recognition, Semantics, Training, Semantic segmentation,
weakly-supervised semantic segmentation
BibRef
Saleh, F.S.[Fatemehsadat S.],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Bringing Background into the Foreground: Making All Classes Equal in
Weakly-Supervised Video Semantic Segmentation,
ICCV17(2125-2135)
IEEE DOI
1802
image classification, image segmentation,
learning (artificial intelligence), video signal processing,
Semantics
BibRef
Lin, G.,
Shen, C.,
van den Hengel, A.J.[Anton J.],
Reid, I.D.,
Exploring Context with Deep Structured Models for Semantic
Segmentation,
PAMI(40), No. 6, June 2018, pp. 1352-1366.
IEEE DOI
1805
BibRef
Earlier:
Efficient Piecewise Training of Deep Structured Models for Semantic
Segmentation,
CVPR16(3194-3203)
IEEE DOI
1612
Context, Context modeling, Image resolution, Image segmentation,
Neural networks, Semantics, Training, Semantic segmentation,
convolutional neural networks
BibRef
Lin, G.,
Milan, A.,
Shen, C.,
Reid, I.D.,
RefineNet: Multi-path Refinement Networks for High-Resolution
Semantic Segmentation,
CVPR17(5168-5177)
IEEE DOI
1711
Computer architecture, Convolution, Image resolution,
Image segmentation, Semantics, Training
BibRef
Zhang, Y.[Yu],
Chen, X.W.[Xiao-Wu],
Li, J.[Jia],
Wang, C.[Chen],
Xia, C.Q.[Chang-Qun],
Li, J.[Jun],
Semantic Object Segmentation in Tagged Videos via Detection,
PAMI(40), No. 7, July 2018, pp. 1741-1754.
IEEE DOI
1806
Image segmentation, Motion segmentation, Object segmentation,
Proposals, Semantics, Spatiotemporal phenomena, Videos,
weakly supervised segmentation
BibRef
Zhang, Y.[Yu],
Chen, X.W.[Xiao-Wu],
Li, J.[Jia],
Teng, W.[Wei],
Song, H.[Haokun],
Exploring Weakly Labeled Images for Video Object Segmentation With
Submodular Proposal Selection,
IP(27), No. 9, September 2018, pp. 4245-4259.
IEEE DOI
1807
computer vision, data mining, image matching, image segmentation,
learning (artificial intelligence), video signal processing,
weakly labeled video
BibRef
Zhang, Y.[Yu],
Chen, X.W.[Xiao-Wu],
Li, J.[Jia],
Wang, C.[Chen],
Xia, C.Q.[Chang-Qun],
Semantic object segmentation via detection in weakly labeled video,
CVPR15(3641-3649)
IEEE DOI
1510
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
Volpi, M.[Michele],
Tuia, D.[Devis],
Deep multi-task learning for a geographically-regularized semantic
segmentation of aerial images,
PandRS(144), 2018, pp. 48-60.
Elsevier DOI
1809
Semantic segmentation, Semantic boundary detection,
Convolutional neural networks, Conditional random fields,
Aerial imagery
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
Chen, B.,
Gong, C.,
Yang, J.,
Importance-Aware Semantic Segmentation for Autonomous Vehicles,
ITS(20), No. 1, January 2019, pp. 137-148.
IEEE DOI
1901
Image segmentation, Autonomous vehicles, Roads, Neural networks,
Feature extraction, Semantics, Reliability, Semantic segmentation,
autonomous driving
BibRef
Fu, K.[Kun],
Lu, W.X.[Wan-Xuan],
Diao, W.H.[Wen-Hui],
Yan, M.L.[Meng-Long],
Sun, H.[Hao],
Zhang, Y.[Yi],
Sun, X.[Xian],
WSF-NET: Weakly Supervised Feature-Fusion Network for Binary
Segmentation in Remote Sensing Image,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
1901
BibRef
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
Nguyen, T.V.,
Nguyen, K.,
Do, T.,
Semantic Prior Analysis for Salient Object Detection,
IP(28), No. 6, June 2019, pp. 3130-3141.
IEEE DOI
1905
Semantics, Object detection, Image color analysis, Deep learning,
Saliency detection, Task analysis, Visualization,
deep networks
BibRef
Redondo-Cabrera, C.,
Baptista-Ríos, M.,
López-Sastre, R.J.,
Learning to Exploit the Prior Network Knowledge for Weakly Supervised
Semantic Segmentation,
IP(28), No. 7, July 2019, pp. 3649-3661.
IEEE DOI
1906
Image segmentation, Semantics, Training, Task analysis, Data models,
Training data, Tools, Semantic segmentation, weakly supervised, deep learning
BibRef
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
Guo, D.,
Pei, Y.,
Zheng, K.,
Yu, H.,
Lu, Y.,
Wang, S.,
Degraded Image Semantic Segmentation With Dense-Gram Networks,
IP(29), No. 1, 2020, pp. 782-795.
IEEE DOI
1910
Image segmentation, Semantics, Degradation, Training,
Motion segmentation, Image restoration, Image texture,
degraded images
BibRef
Wang, K.,
Wang, C.,
Tai, T.,
Wang, J.,
Object Bounding Transformed Network for End-to-End Semantic
Segmentation,
ICIP19(3217-3221)
IEEE DOI
1910
image semantic segmentation, Object Boundary Guide,
Doman Transform Network, ResNet 101
BibRef
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
Couprie, C.[Camille],
Luc, P.[Pauline],
Verbeek, J.[Jakob],
Joint Future Semantic and Instance Segmentation Prediction,
AnticipateBeh18(III:154-168).
Springer DOI
1905
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.[Fengmao],
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
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
Halupka, K.,
Garnavi, R.,
Moore, S.,
Deep Semantic Instance Segmentation of Tree-Like Structures Using
Synthetic Data,
WACV19(1713-1722)
IEEE DOI
1904
data analysis, feature extraction, image segmentation,
learning (artificial intelligence), neural nets,
Periodic structures
BibRef
Follmann, P.[Patrick],
Nig, R.K.[Rebecca Kö],
Rtinger, P.H.[Philipp Hä],
Klostermann, M.[Michael],
Ttger, T.B.[Tobias Bö],
Learning to See the Invisible: End-to-End Trainable Amodal Instance
Segmentation,
WACV19(1328-1336)
IEEE DOI
1904
image segmentation, learning (artificial intelligence),
object detection, COCOA cls, D2S amodal, COCO amodal dataset,
BibRef
Pham, Q.,
Hua, B.,
Nguyen, T.,
Yeung, S.,
Real-Time Progressive 3D Semantic Segmentation for Indoor Scenes,
WACV19(1089-1098)
IEEE DOI
1904
image reconstruction, image segmentation, time progressive 3D,
widespread adoption, autonomous systems, assistant robots,
Neural networks
BibRef
Zhong, M.,
Zeng, G.,
Efficient Object Region Discovery for Weakly-supervised Semantic
Segmentation,
ICPR18(2166-2171)
IEEE DOI
1812
Image segmentation, Training, Semantics, Benchmark testing,
Convolutional neural networks, Task analysis, Standards
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
Liang, X.D.[Xiao-Dan],
Xing, E.[Eric],
Zhou, H.F.[Hong-Fei],
Dynamic-Structured Semantic Propagation Network,
CVPR18(752-761)
IEEE DOI
1812
Semantics, Neurons, Task analysis, Image segmentation, Vocabulary,
Training, Correlation
BibRef
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
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
Ahn, J.,
Kwak, S.,
Learning Pixel-Level Semantic Affinity with Image-Level Supervision
for Weakly Supervised Semantic Segmentation,
CVPR18(4981-4990)
IEEE DOI
1812
Image segmentation, Semantics, Training, Shape, Visualization,
Pipelines, Motion segmentation
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
Yu, C.Q.[Chang-Qian],
Wang, J.B.[Jing-Bo],
Peng, C.[Chao],
Gao, C.X.[Chang-Xin],
Yu, G.[Gang],
Sang, N.[Nong],
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic
Segmentation,
ECCV18(XIII: 334-349).
Springer DOI
1810
BibRef
Huang, P.Y.[Po-Yu],
Hsu, W.T.[Wan-Ting],
Chiu, C.Y.[Chun-Yueh],
Wu, T.F.[Ting-Fan],
Sun, M.[Min],
Efficient Uncertainty Estimation for Semantic Segmentation in Videos,
ECCV18(I: 536-552).
Springer DOI
1810
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.[Xinge],
Zhou, H.[Hui],
Yang, C.[Ceyuan],
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
Fan, R.C.[Ruo-Chen],
Hou, Q.[Qibin],
Cheng, M.M.[Ming-Ming],
Yu, G.[Gang],
Martin, R.R.[Ralph R.],
Hu, S.M.[Shi-Min],
Associating Inter-image Salient Instances for Weakly Supervised
Semantic Segmentation,
ECCV18(IX: 371-388).
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
Dai, A.[Angela],
Nießner, M.[Matthias],
3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation,
ECCV18(X: 458-474).
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
Xiao, C.W.[Chao-Wei],
Deng, R.Z.[Rui-Zhi],
Li, B.[Bo],
Yu, F.[Fisher],
Liu, M.Y.[Ming-Yan],
Song, D.[Dawn],
Characterizing Adversarial Examples Based on Spatial Consistency
Information for Semantic Segmentation,
ECCV18(X: 220-237).
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
Wilhelm, T.,
Grzeszick, R.,
Fink, G.A.,
Woehler, C.,
From Weakly Supervised Object Localization to Semantic Segmentation
by Probabilistic Image Modeling,
DICTA17(1-7)
IEEE DOI
1804
image segmentation, learning (artificial intelligence),
object detection, convolutional network, deep learning,
Training
BibRef
Zhang, Y.,
Ngan, K.N.,
Huynh, C.P.,
Habili, N.,
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
Xie, C.,
Wang, J.,
Zhang, Z.,
Zhou, Y.,
Xie, L.,
Yuille, A.L.[Alan L.],
Adversarial Examples for Semantic Segmentation and Object Detection,
ICCV17(1378-1387)
IEEE DOI
1802
image classification, image segmentation, object detection,
pattern clustering, adversarial perturbations,
Semantics
BibRef
Zhang, Y.,
David, P.,
Gong, B.,
Curriculum Domain Adaptation for Semantic Segmentation of Urban
Scenes,
ICCV17(2039-2049)
IEEE DOI
1802
computer graphics, convolution, image classification,
image segmentation, learning (artificial intelligence),
Training
BibRef
Luc, P.[Pauline],
Couprie, C.[Camille],
Le Cun, Y.[Yann],
Verbeek, J.[Jakob],
Predicting Future Instance Segmentation by Forecasting Convolutional
Features,
ECCV18(IX: 593-608).
Springer DOI
1810
BibRef
Luc, P.[Pauline],
Neverova, N.,
Couprie, C.[Camille],
Verbeek, J.[Jakob],
Le Cun, Y.[Yann],
Predicting Deeper into the Future of Semantic Segmentation,
ICCV17(648-657)
IEEE DOI
1802
feedforward neural nets, image colour analysis, image resolution,
image segmentation, image sequences,
Training
BibRef
Peng, C.,
Zhang, X.,
Yu, G.,
Luo, G.,
Sun, J.,
Large Kernel Matters:
Improve Semantic Segmentation by Global Convolutional Network,
CVPR17(1743-1751)
IEEE DOI
1711
Computational modeling, Feature extraction, Image segmentation,
Kernel, Semantics, Standards
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
Pohlen, T.,
Hermans, A.[Alexander],
Mathias, M.,
Leibe, B.[Bastian],
Full-Resolution Residual Networks for Semantic Segmentation in Street
Scenes,
CVPR17(3309-3318)
IEEE DOI
1711
Computer architecture, Image segmentation, Network architecture,
Semantics, Streaming media, Training
BibRef
Li, Y.,
Qi, H.,
Dai, J.,
Ji, X.,
Wei, Y.,
Fully Convolutional Instance-Aware Semantic Segmentation,
CVPR17(4438-4446)
IEEE DOI
1711
Convolution, Convolutional codes, Image segmentation,
Object segmentation, Proposals, Semantics
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
He, Y.[Yang],
Chiu, W.C.,
Keuper, M.[Margret],
Fritz, M.[Mario],
STD2P:
RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling,
CVPR17(7158-7167)
IEEE DOI
1711
Image segmentation, Indexes, Optical imaging, Semantics,
Training, Videos
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
Namin, S.R.,
Alvarez, J.M.,
Petersson, L.,
2D-3D semantic segmentation using cardinality as higher-order loss,
ICPR16(3775-3780)
IEEE DOI
1705
Image edge detection, Image segmentation, Labeling, Sensors,
Training.
BibRef
Wang, H.L.[Hui-Ling],
Raiko, T.[Tapani],
Lensu, L.[Lasse],
Wang, T.H.[Ting-Huai],
Karhunen, J.[Juha],
Semi-supervised Domain Adaptation for Weakly Labeled Semantic Video
Object Segmentation,
ACCV16(I: 163-179).
Springer DOI
1704
BibRef
Audebert, N.[Nicolas],
Le Saux, B.[Bertrand],
Lefčvre, S.[Sébastien],
Joint Learning from Earth Observation and OpenStreetMap Data to Get
Faster Better Semantic Maps,
EarthVision17(1552-1560)
IEEE DOI
1709
BibRef
Earlier:
Semantic Segmentation of Earth Observation Data Using Multimodal and
Multi-scale Deep Networks,
ACCV16(I: 180-196).
Springer DOI
1704
Buildings, Labeling, Optical imaging, Roads, Semantics, Sensors, Training
BibRef
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
Hong, S.,
Oh, J.,
Lee, H.,
Han, B.,
Learning Transferrable Knowledge for Semantic Segmentation with Deep
Convolutional Neural Network,
CVPR16(3204-3212)
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
Qi, G.J.[Guo-Jun],
Hierarchically Gated Deep Networks for Semantic Segmentation,
CVPR16(2267-2275)
IEEE DOI
1612
BibRef
Li, K.[Ke],
Malik, J.[Jitendra],
Amodal Instance Segmentation,
ECCV16(II: 677-693).
Springer DOI
1611
predict the region encompassing both visible and occluded parts of each object.
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
Shimoda, W.[Wataru],
Yanai, K.[Keiji],
Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic
Segmentation,
ECCV16(IV: 218-234).
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
Krapac, J.[Josip],
egvic, I.K.S.[I.K. Sinia],
Ladder-Style DenseNets for Semantic Segmentation of Large Natural
Images,
CVRoads17(238-245)
IEEE DOI
1802
BibRef
Earlier:
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores
Back to the Pixels,
GCPR16(377-388).
Springer DOI
1611
Convolution, Image segmentation, Semantics, Spatial resolution,
Tensile stress, Training
BibRef
Kreo, I.[Ivan],
Cauevic, D.[Denis],
Krapac, J.[Josip],
egvic, I.K.S.[I.K. Sinia],
Convolutional Scale Invariance for Semantic Segmentation,
GCPR16(64-75).
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
Ran, L.Y.[Ling-Yan],
Zhang, Y.N.[Yan-Ning],
Hua, G.[Gang],
CANNET: Context aware nonlocal convolutional networks for semantic
image segmentation,
ICIP15(4669-4673)
IEEE DOI
1512
Semantic segmentation; context aware module; sparse kernel
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
Samrouth, K.,
Deforges, O.,
Liu, Y.[Yi],
Falou, W.,
Khalil, M.,
A joint 3D image semantic segmentation and scalable coding scheme
with ROI approach,
VCIP14(270-273)
IEEE DOI
1504
data compression
BibRef
Namin, S.T.[Sarah Taghavi],
Najafi, M.[Mohammad],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
A Multi-modal Graphical Model for Scene Analysis,
WACV15(1006-1013)
IEEE DOI
1503
Graphical models
2D-3D data. Semantic segmentation.
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
Mustikovela, S.K.[Siva Karthik],
Yang, M.Y.[Michael Ying],
Rother, C.[Carsten],
Can Ground Truth Label Propagation from Video Help Semantic
Segmentation?,
VSeg16(III: 804-820).
Springer DOI
1611
BibRef
Zheng, S.[Shuai],
Cheng, M.M.[Ming-Ming],
Warrell, J.[Jonathan],
Sturgess, P.[Paul],
Vineet, V.[Vibhav],
Rother, C.[Carsten],
Torr, P.H.S.[Philip H.S.],
Dense Semantic Image Segmentation with Objects and Attributes,
CVPR14(3214-3221)
IEEE DOI
1409
Attributes; Image Segmentation; Object Recognition; Scene Understanding
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
Chang, F.J.[Feng-Ju],
Lin, Y.Y.[Yen-Yu],
Hsu, K.J.[Kuang-Jui],
Multiple Structured-Instance Learning for Semantic Segmentation with
Uncertain Training Data,
CVPR14(360-367)
IEEE DOI
1409
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
Kundu, A.[Abhijit],
Li, Y.[Yin],
Dellaert, F.[Frank],
Li, F.X.[Fu-Xin],
Rehg, J.M.[James M.],
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video,
ECCV14(VI: 703-718).
Springer DOI
1408
BibRef
Inagaki, S.[Shun],
Imiya, A.[Atsushi],
Semantic Segmentation of Low Frame-Rate Image Sequence Using
Statistical Properties of Optical Flow for Remote Exploration,
ISVC14(I: 477-488).
Springer DOI
1501
BibRef
And:
Statistical Method for Semantic Segmentation of Dominant Plane from
Remote Exploration Image Sequence,
SSSPR14(263-272).
Springer DOI
1408
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
BibRef
Li, K.[Ke],
Hariharan, B.[Bharath],
Malik, J.[Jitendra],
Iterative Instance Segmentation,
CVPR16(3659-3667)
IEEE DOI
1612
BibRef
Lin, D.[Dahua],
Fidler, S.[Sanja],
Urtasun, R.[Raquel],
Holistic Scene Understanding for 3D Object Detection with RGBD
Cameras,
ICCV13(1417-1424)
IEEE DOI
1403
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
Vezhnevets, A.[Alexander],
Ferrari, V.[Vittorio],
Associative Embeddings for Large-Scale Knowledge Transfer with
Self-Assessment,
CVPR14(1987-1994)
IEEE DOI
1409
ImageNet
BibRef
Vezhnevets, A.[Alexander],
Buhmann, J.M.[Joachim M.],
Ferrari, V.[Vittorio],
Active learning for semantic segmentation with expected change,
CVPR12(3162-3169).
IEEE DOI
1208
BibRef
Vezhnevets, A.[Alexander],
Ferrari, V.[Vittorio],
Buhmann, J.M.[Joachim M.],
Weakly supervised structured output learning for semantic segmentation,
CVPR12(845-852).
IEEE DOI
1208
BibRef
Earlier:
Weakly supervised semantic segmentation with a multi-image model,
ICCV11(643-650).
IEEE DOI
1201
BibRef
Vezhnevets, A.[Alexander],
Buhmann, J.M.[Joachim M.],
Towards weakly supervised semantic segmentation by means of multiple
instance and multitask learning,
CVPR10(3249-3256).
IEEE DOI
1006
See also Agnostic Domain Adaptation.
BibRef
Dunlop, H.[Heather],
Scene classification of images and video via semantic segmentation,
POCV10(72-79).
IEEE DOI
1006
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],
Leibe, B.[Bastian],
Joint 2D-3D temporally consistent semantic segmentation of street
scenes,
CVPR12(2823-2830).
IEEE DOI
1208
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
Micusik, B.[Branislav],
Kosecka, J.[Jana],
Semantic segmentation of street scenes by superpixel co-occurrence and
3D geometry,
ObjectEvent09(625-632).
IEEE DOI
0910
identify as one of a few common object/background classes.
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
Fua and Leclerc Guided Segmentation Papers .