8.6.1.2 Panoptic Segmentation

Chapter Contents (Back)
Panoptic Segmentation. Perform instance segmentation for foreground instances and semantic segmentation for background simultaneously.
See also Instance Segmentation.

Chen, Q.[Qiang], Cheng, A.[Anda], He, X.Y.[Xiang-Yu], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
SpatialFlow: Bridging All Tasks for Panoptic Segmentation,
CirSysVideo(31), No. 6, June 2021, pp. 2288-2300.
IEEE DOI 2106
Task analysis, Image segmentation, Head, Object detection, Detectors, Semantics, Benchmark testing, Panoptic segmentation, location-aware BibRef

Chu, T.[Tao], Cai, W.J.[Wen-Jie], Liu, Q.[Qiong],
Learning panoptic segmentation through feature discriminability,
PR(122), 2022, pp. 108240.
Elsevier DOI 2112
Panoptic segmentation, Feature discriminability, Region refinement BibRef

de Carvalho, O.L.F.[Osmar Luiz Ferreira], de Carvalho Júnior, O.A.[Osmar Abílio], Rosa e Silva, C.[Cristiano], de Albuquerque, A.O.[Anesmar Olino], Santana, N.C.[Nickolas Castro], Borges, D.L.[Dibio Leandro], Gomes, R.A.T.[Roberto Arnaldo Trancoso], Guimarăes, R.F.[Renato Fontes],
Panoptic Segmentation Meets Remote Sensing,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, W.Q.[Wei-Qi], You, X.[Xiong], Yang, J.[Jian], Su, M.Z.[Ming-Zhan], Zhang, L.T.[Lan-Tian], Yang, Z.K.[Zhen-Kai], Kuang, Y.C.[Ying-Cai],
LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Kim, D.[Dahun], Woo, S.[Sanghyun], Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Dense Pixel-Level Interpretation of Dynamic Scenes With Video Panoptic Segmentation,
IP(31), 2022, pp. 5383-5395.
IEEE DOI 2208
Task analysis, Image segmentation, Measurement, Electron tubes, Semantics, Head, Benchmark testing, Video panoptic segmentation, scene parsing BibRef

Lv, K.F.[Ke-Feng], Zhang, Y.S.[Yong-Sheng], Yu, Y.[Ying], Zhang, Z.C.[Zhen-Chao], Li, L.[Lei],
Visual Localization and Target Perception Based on Panoptic Segmentation,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Tian, Z.[Zhi], Zhang, B.[Bowen], Chen, H.[Hao], Shen, C.H.[Chun-Hua],
Instance and Panoptic Segmentation Using Conditional Convolutions,
PAMI(45), No. 1, January 2023, pp. 669-680.
IEEE DOI 2212
Head, Magnetic heads, Image segmentation, Task analysis, Semantics, Convolutional codes, Detectors, Fully convolutional networks, panoptic segmentation BibRef

Wang, L.[Le], Liu, H.Z.[Hong-Zhen], Zhou, S.P.[San-Ping], Tang, W.[Wei], Hua, G.[Gang],
Instance Motion Tendency Learning for Video Panoptic Segmentation,
IP(32), 2023, pp. 764-778.
IEEE DOI 2301
Image segmentation, Motion segmentation, Task analysis, Tracking, Optical flow, Transformers, Target tracking, deep neural network BibRef

Chang, S.E.[Shuo-En], Chen, Y.[Yi], Yang, Y.C.[Yi-Cheng], Lin, E.T.[En-Ting], Hsiao, P.Y.[Pei-Yung], Fu, L.C.[Li-Chen],
SE-PSNet: Silhouette-based Enhancement Feature for Panoptic Segmentation Network,
JVCIR(90), 2023, pp. 103736.
Elsevier DOI 2301
Deep learning, Panoptic segmentation, Instance segmentation, Silhouette, confidence score BibRef


Fan, J.S.[Jun-Song], Zhang, Z.X.[Zhao-Xiang], Tan, T.N.[Tie-Niu],
Pointly-Supervised Panoptic Segmentation,
ECCV22(XXX:319-336).
Springer DOI 2211
BibRef

Xu, S.L.[Shi-Lin], Li, X.[Xiangtai], Yang, Y.[Yibo], Li, H.Y.[Hong-Yang], Cheng, G.L.[Guang-Liang], Tong, Y.[Yunhai],
Query Learning of Both Thing and Stuff for Panoptic Segmentation,
ICIP22(716-720)
IEEE DOI 2211
Training, Image segmentation, Schedules, Image coding, Design methodology, Pipelines, Semantics, Panoptic segmentation, Computer vision BibRef

Liu, Q.F.[Qing-Feng], El-Khamy, M.[Mostafa],
Panoptic-Deeplab-DVA: Improving Panoptic Deeplab with Dual Value Attention and Instance Boundary Aware Regression,
ICIP22(3888-3892)
IEEE DOI 2211
Training, Performance evaluation, Mobile handsets, Complexity theory, Task analysis, Information exchange, Panoptic DeepLab BibRef

Mei, J.[Jieru], Zhu, A.Z.[Alex Zihao], Yan, X.C.[Xin-Chen], Yan, H.[Hang], Qiao, S.Y.[Si-Yuan], Chen, L.C.[Liang-Chieh], Kretzschmar, H.[Henrik],
Waymo Open Dataset: Panoramic Video Panoptic Segmentation,
ECCV22(XXIX:53-72).
Springer DOI 2211
BibRef

Li, X.[Xiangtai], Xu, S.L.[Shi-Lin], Yang, Y.[Yibo], Cheng, G.L.[Guang-Liang], Tong, Y.[Yunhai], Tao, D.C.[Da-Cheng],
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation,
ECCV22(XXVII:729-747).
Springer DOI 2211
BibRef

Yuan, H.[Haobo], Li, X.[Xiangtai], Yang, Y.[Yibo], Cheng, G.L.[Guang-Liang], Zhang, J.[Jing], Tong, Y.[Yunhai], Zhang, L.[Lefei], Tao, D.C.[Da-Cheng],
PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation,
ECCV22(XXVII:582-599).
Springer DOI 2211
BibRef

Kundu, A.[Abhijit], Genova, K.[Kyle], Yin, X.Q.[Xiao-Qi], Fathi, A.[Alireza], Pantofaru, C.[Caroline], Guibas, L.J.[Leonidas J.], Tagliasacchi, A.[Andrea], Dellaert, F.[Frank], Funkhouser, T.[Thomas],
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation,
CVPR22(12861-12871)
IEEE DOI 2210
Image segmentation, Solid modeling, Semantics, Color, Predictive models, Rendering (computer graphics), Scene analysis and understanding BibRef

Zhou, Y.[Yi], Zhang, H.[Hui], Lee, H.[Hana], Sun, S.[Shuyang], Li, P.J.[Ping-Jun], Zhu, Y.G.[Yang-Guang], Yoo, B.I.[Byung-In], Qi, X.J.[Xiao-Juan], Han, J.J.[Jae-Joon],
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation,
CVPR22(3083-3093)
IEEE DOI 2210
Representation learning, Tracking, Semantics, Pipelines, Benchmark testing, Pattern recognition, Motion and tracking BibRef

Graber, C.[Colin], Jazra, C.[Cyril], Luo, W.J.[Wen-Jie], Gui, L.[Liangyan], Schwing, A.[Alexander],
Joint Forecasting of Panoptic Segmentations with Difference Attention,
CVPR22(2617-2626)
IEEE DOI 2210
BibRef
And: Precognition22(2558-2567)
IEEE DOI 2210
Measurement, Image analysis, Shape, Predictive models, Transformers, Pattern recognition, Scene analysis and understanding, grouping and shape analysis BibRef

Gao, N.[Naiyu], He, F.[Fei], Jia, J.[Jian], Shan, Y.[Yanhu], Zhang, H.Y.[Hao-Yang], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi],
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation,
CVPR22(1622-1632)
IEEE DOI 2210
Image segmentation, Head, Semantics, Estimation, Lead, Pattern recognition, 3D from single images, Video analysis and understanding BibRef

Borse, S.[Shubhankar], Park, H.[Hyojin], Cai, H.[Hong], Das, D.[Debasmit], Garrepalli, R.[Risheek], Porikli, F.[Fatih],
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation,
CVPR22(1259-1269)
IEEE DOI 2210
Visualization, Roads, Semantics, Computer architecture, Benchmark testing, Feature extraction, Segmentation, Representation learning BibRef

Fazlali, H.[Hamidreza], Xu, Y.X.[Yi-Xuan], Ren, Y.[Yuan], Liu, B.B.[Bing-Bing],
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation,
CVPR22(17171-17180)
IEEE DOI 2210
Heating systems, Laser radar, Semantics, Object detection, Performance gain, Feature extraction, Vision applications and systems BibRef

Mohan, R.[Rohit], Valada, A.[Abhinav],
Amodal Panoptic Segmentation,
CVPR22(20991-21000)
IEEE DOI 2210
Measurement, Computational modeling, Semantics, Computer architecture, Benchmark testing, Pattern recognition, Scene analysis and understanding BibRef

Miao, J.[Jiaxu], Wang, X.H.[Xiao-Han], Wu, Y.[Yu], Li, W.[Wei], Zhang, X.[Xu], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi],
Large-scale Video Panoptic Segmentation in the Wild: A Benchmark,
CVPR22(21001-21011)
IEEE DOI 2210
Annotations, Shape, Semantics, Benchmark testing, Pattern recognition, Task analysis, Datasets and evaluation, grouping and shape analysis BibRef

Zendel, O.[Oliver], Schörghuber, M.[Matthias], Rainer, B.[Bernhard], Murschitz, M.[Markus], Beleznai, C.[Csaba],
Unifying Panoptic Segmentation for Autonomous Driving,
CVPR22(21319-21328)
IEEE DOI 2210
Training, Visualization, Semantics, Data visualization, Benchmark testing, Licenses, Robustness, Datasets and evaluation, grouping and shape analysis BibRef

Chen, Q.[Qi], Vora, S.[Sourabh],
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity,
WAD22(4528-4535)
IEEE DOI 2210
Laser radar, Semantics, Merging, Clustering algorithms, Object detection BibRef

Li, Z.Q.[Zhi-Qi], Wang, W.[Wenhai], Xie, E.[Enze], Yu, Z.D.[Zhi-Ding], Anandkumar, A.[Anima], Alvarez, J.M.[Jose M.], Luo, P.[Ping], Lu, T.[Tong],
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers,
CVPR22(1270-1279)
IEEE DOI 2210
Training, Image segmentation, Costs, Semantics, Interference, Transformers, Segmentation, grouping and shape analysis, Scene analysis and understanding BibRef

Li, J.[Jinke], He, X.[Xiao], Wen, Y.[Yang], Gao, Y.[Yuan], Cheng, X.Q.[Xiao-Qiang], Zhang, D.[Dan],
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap,
CVPR22(11799-11808)
IEEE DOI 2210
Heating systems, Laser radar, Fuses, Shape, Navigation, Semantics, grouping and shape analysis, Segmentation BibRef

Raivio, L.[Leevi], Rahtu, E.[Esa],
Online Panoptic 3D Reconstruction as a Linear Assignment Problem,
CIAP22(II:39-50).
Springer DOI 2205
BibRef

Quattrocchi, C.[Camillo], Mauro, D.D.[Daniele Di], Furnari, A.[Antonino], Farinella, G.M.[Giovanni Maria],
Panoptic Segmentation in Industrial Environments Using Synthetic and Real Data,
CIAP22(II:275-286).
Springer DOI 2205
BibRef

Hwang, S.[Sukjun], Oh, S.W.[Seoung Wug], Kim, S.J.[Seon Joo],
Single-shot Path Integrated Panoptic Segmentation,
WACV22(1939-1948)
IEEE DOI 2202
Computational modeling, Semantics, Benchmark testing, Information filters, Task analysis, Scene Understanding BibRef

Petrovai, A.[Andra], Nedevschi, S.[Sergiu],
Time-Space Transformers for Video Panoptic Segmentation,
WACV22(2643-2652)
IEEE DOI 2202
Image resolution, Correlation, Computational modeling, Aggregates, Semantics, Computer architecture, Transformers, Segmentation, Vision Systems and Applications BibRef

Zhao, Y.M.[Yi-Ming], Zhang, X.[Xiao], Huang, X.M.[Xin-Ming],
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation,
TradiCV21(2464-2473)
IEEE DOI 2112
Deep learning, Laser radar, Codes, Semantics, Pipelines, Clustering algorithms BibRef

Kerola, T.[Tommi], Li, J.[Jie], Kanehira, A.[Atsushi], Kudo, Y.[Yasunori], Vallet, A.[Alexis], Gaidon, A.[Adrien],
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation,
CVPR21(14408-14418)
IEEE DOI 2111
Semantics, Fasteners, Predictive models, Ontologies, Stability analysis, Pattern recognition, Proposals BibRef

Shen, Y.H.[Yun-Hang], Cao, L.J.[Liu-Juan], Chen, Z.W.[Zhi-Wei], Lian, F.H.[Fei-Hong], Zhang, B.C.[Bao-Chang], Su, C.[Chi], Wu, Y.J.[Yong-Jian], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation,
CVPR21(16689-16700)
IEEE DOI 2111
Location awareness, Image segmentation, Semantics, Spatial coherence, Object detection, Feature extraction BibRef

Zhou, Z.X.[Zi-Xiang], Zhang, Y.[Yang], Foroosh, H.[Hassan],
Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation,
CVPR21(13189-13198)
IEEE DOI 2111
Laser radar, Semantics, Real-time systems, Pattern recognition, Complexity theory BibRef

de Geus, D.[Daan], Meletis, P.[Panagiotis], Lu, C.Y.[Chen-Yang], Wen, X.X.[Xiao-Xiao], Dubbelman, G.[Gijs],
Part-aware Panoptic Segmentation,
CVPR21(5481-5490)
IEEE DOI 2111
Measurement, Training, Technological innovation, Codes, Annotations, Merging BibRef

Yu, Q.H.[Qi-Hang], Wang, H.Y.[Hui-Yu], Kim, D.[Dahun], Qiao, S.Y.[Si-Yuan], Collins, M.[Maxwell], Zhu, Y.K.[Yu-Kun], Adam, H.[Hartwig], Yuille, A.Y.[Alan Y.], Chen, L.C.[Liang-Chieh],
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation,
CVPR22(2550-2560)
IEEE DOI 2210
Art, Computer architecture, Transformers, Pattern recognition, Task analysis, Segmentation, grouping and shape analysis BibRef

Wang, H.Y.[Hui-Yu], Zhu, Y.K.[Yu-Kun], Adam, H.[Hartwig], Yuille, A.L.[Alan L.], Chen, L.C.[Liang-Chieh],
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers,
CVPR21(5459-5470)
IEEE DOI 2111
Merging, Pipelines, Computer architecture, Transformers, Pattern recognition, Task analysis BibRef

Qiao, S.Y.[Si-Yuan], Zhu, Y.K.[Yu-Kun], Adam, H.[Hartwig], Yuille, A.L.[Alan L.], Chen, L.C.[Liang-Chieh],
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation,
CVPR21(3996-4007)
IEEE DOI 2111
Measurement, Solid modeling, Semantics, Estimation, Predictive models, Pattern recognition BibRef

Woo, S.[Sanghyun], Kim, D.[Dahun], Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Learning to Associate Every Segment for Video Panoptic Segmentation,
CVPR21(2704-2713)
IEEE DOI 2111
Learning systems, Computational modeling, Linear programming, Pattern recognition, Proposals, Task analysis BibRef

Hwang, J.[Jaedong], Oh, S.W.[Seoung Wug], Lee, J.Y.[Joon-Young], Han, B.H.[Bo-Hyung],
Exemplar-Based Open-Set Panoptic Segmentation Network,
CVPR21(1175-1184)
IEEE DOI 2111
Training, Image segmentation, Solid modeling, Benchmark testing, Solids, Pattern recognition BibRef

Li, Y.W.[Yan-Wei], Zhao, H.S.[Heng-Shuang], Qi, X.J.[Xiao-Juan], Wang, L.W.[Li-Wei], Li, Z.M.[Ze-Ming], Sun, J.[Jian], Jia, J.Y.[Jia-Ya],
Fully Convolutional Networks for Panoptic Segmentation,
CVPR21(214-223)
IEEE DOI 2111
Convolutional codes, Location awareness, Semantics, Pipelines, Generators, Pattern recognition BibRef

Aygün, M.[Mehmet], Ošep, A.[Aljoša], Weber, M.[Mark], Maximov, M.[Maxim], Stachniss, C.[Cyrill], Behley, J.[Jens], Leal-Taixé, L.[Laura],
4D Panoptic LiDAR Segmentation,
CVPR21(5523-5533)
IEEE DOI 2111
Measurement, Laser radar, Roads, Computational modeling, Semantics, Benchmark testing BibRef

Porzi, L.[Lorenzo], Bulň, S.R.[Samuel Rota], Kontschieder, P.[Peter],
Improving Panoptic Segmentation at All Scales,
CVPR21(7298-7307)
IEEE DOI 2111
Training, Measurement, Image segmentation, Image resolution, Memory management, Crops BibRef

Huang, J.X.[Jia-Xing], Guan, D.[Dayan], Xiao, A.[Aoran], Lu, S.J.[Shi-Jian],
Cross-View Regularization for Domain Adaptive Panoptic Segmentation,
CVPR21(10128-10139)
IEEE DOI 2111
Image segmentation, Adaptive systems, Semantics, Supervised learning, Pattern recognition, Task analysis BibRef

Graber, C.[Colin], Tsai, G.[Grace], Firman, M.[Michael], Brostow, G.[Gabriel], Schwing, A.[Alexander],
Panoptic Segmentation Forecasting,
CVPR21(12512-12521)
IEEE DOI 2111
Image segmentation, Motion segmentation, Semantics, Dynamics, Predictive models, Cameras, Real-time systems BibRef

Hong, F.Z.[Fang-Zhou], Zhou, H.[Hui], Zhu, X.G.[Xin-Ge], Li, H.S.[Hong-Sheng], Liu, Z.[Ziwei],
LiDAR-based Panoptic Segmentation via Dynamic Shifting Network,
CVPR21(13085-13094)
IEEE DOI 2111
Measurement, Laser radar, Semantics, Feature extraction, Robustness, Sensors BibRef

Hong, W.X.[Wei-Xiang], Guo, Q.[Qingpei], Zhang, W.[Wei], Chen, J.D.[Jing-Dong], Chu, W.[Wei],
LPSNet: A lightweight solution for fast panoptic segmentation,
CVPR21(16741-16749)
IEEE DOI 2111
Costs, Semantics, Memory management, Pipelines, Object detection, Real-time systems BibRef

Bonde, U.[Ujwal], Alcantarilla, P.F.[Pablo F.], Leutenegger, S.[Stefan],
Towards Bounding-Box Free Panoptic Segmentation,
GCPR20(316-330).
Springer DOI 2110
BibRef

Graber, C.[Colin], Tsai, G.[Grace], Firman, M.[Michael], Brostow, G.[Gabriel], Schwing, A.[Alexander],
Panoptic Segmentation Forecasting,
Precognition21(2279-2288)
IEEE DOI 2109
Image segmentation, Motion segmentation, Semantics, Dynamics, Predictive models, Cameras, Real-time systems BibRef

Chang, C.Y.[Chia-Yuan], Chang, S.E.[Shuo-En], Hsiao, P.Y.[Pei-Yung], Fu, L.C.[Li-Chen],
Epsnet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion,
ACCV20(I:689-705).
Springer DOI 2103
BibRef

Qin, Z.Q.[Ze-Qun], Zhang, P.Y.[Peng-Yi], Wu, F.[Fei], Li, X.[Xi],
FcaNet: Frequency Channel Attention Networks,
ICCV21(763-772)
IEEE DOI 2203
Image segmentation, Codes, Frequency-domain analysis, Computational modeling, Object detection, BibRef

Chen, Y.F.[Yi-Feng], Lin, G.C.[Guang-Chen], Li, S.Y.[Song-Yuan], Bourahla, O.[Omar], Wu, Y.M.[Yi-Ming], Wang, F.F.[Fang-Fang], Feng, J.Y.[Jun-Yi], Xu, M.L.[Ming-Liang], Li, X.[Xi],
BANet: Bidirectional Aggregation Network With Occlusion Handling for Panoptic Segmentation,
CVPR20(3792-3801)
IEEE DOI 2008
Semantics, Image segmentation, Agriculture, Task analysis, Feature extraction, Pipelines, Convolution BibRef

Dundar, A., Sapra, K., Liu, G., Tao, A., Catanzaro, B.,
Panoptic-Based Image Synthesis,
CVPR20(8067-8076)
IEEE DOI 2008
Convolution, Semantics, Image generation, Task analysis, Generators, Image resolution, Windows BibRef

Hou, R., Li, J., Bhargava, A., Raventos, A., Guizilini, V., Fang, C., Lynch, J., Gaidon, A.,
Real-Time Panoptic Segmentation From Dense Detections,
CVPR20(8520-8529)
IEEE DOI 2008
Semantics, Real-time systems, Image segmentation, Task analysis, Object detection, Proposals, Prediction algorithms BibRef

Wu, Y., Zhang, G., Gao, Y., Deng, X., Gong, K., Liang, X., Lin, L.,
Bidirectional Graph Reasoning Network for Panoptic Segmentation,
CVPR20(9077-9086)
IEEE DOI 2008
Image segmentation, Semantics, Cognition, Task analysis, Feature extraction, Visualization, Proposals BibRef

Wang, H., Luo, R., Maire, M., Shakhnarovich, G.,
Pixel Consensus Voting for Panoptic Segmentation,
CVPR20(9461-9470)
IEEE DOI 2008
Semantics, Transforms, Heating systems, Feature extraction, Image segmentation, Task analysis, Training BibRef

Kim, D., Woo, S., Lee, J., Kweon, I.S.,
Video Panoptic Segmentation,
CVPR20(9856-9865)
IEEE DOI 2008
Task analysis, Image segmentation, Electron tubes, Measurement, Semantics, Head BibRef

Lazarow, J., Lee, K., Shi, K., Tu, Z.,
Learning Instance Occlusion for Panoptic Segmentation,
CVPR20(10717-10726)
IEEE DOI 2008
Head, Semantics, Image segmentation, Proposals, Task analysis, Nickel BibRef

Cheng, B., Collins, M.D., Zhu, Y., Liu, T., Huang, T.S., Adam, H., Chen, L.,
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation,
CVPR20(12472-12482)
IEEE DOI 2008
Semantics, Image segmentation, Decoding, Task analysis, Spatial resolution, Convolution, Feature extraction BibRef

Li, Q., Qi, X., Torr, P.H.S.,
Unifying Training and Inference for Panoptic Segmentation,
CVPR20(13317-13325)
IEEE DOI 2008
Semantics, Training, Head, Pipelines, Feature extraction, Object detection, Image segmentation BibRef

Liu, D., Zhang, D., Song, Y., Zhang, F., O'Donnell, L., Huang, H., Chen, M., Cai, W.,
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting,
CVPR20(4242-4251)
IEEE DOI 2008
Image segmentation, Task analysis, Semantics, Microscopy, Adaptation models, Object detection, Training BibRef

Kirillov, A.[Alexander], He, K.[Kaiming], Girshick, R.[Ross], Rother, C.[Carsten], Dollar, P.[Piotr],
Panoptic Segmentation,
CVPR19(9396-9405).
IEEE DOI 2002
BibRef

Liu, H.Y.[Huan-Yu], Peng, C.[Chao], Yu, C.Q.[Chang-Qian], Wang, J.[Jingbo], Liu, X.[Xu], Yu, G.[Gang], Jiang, W.[Wei],
An End-To-End Network for Panoptic Segmentation,
CVPR19(6165-6174).
IEEE DOI 2002
BibRef

Li, Y.W.[Yan-Wei], Chen, X.[Xinze], Zhu, Z.[Zheng], Xie, L.X.[Ling-Xi], Huang, G.[Guan], Du, D.L.[Da-Long], Wang, X.G.[Xin-Gang],
Attention-Guided Unified Network for Panoptic Segmentation,
CVPR19(7019-7028).
IEEE DOI 2002
BibRef

Xiong, Y.[Yuwen], Liao, R.J.[Ren-Jie], Zhao, H.S.[Heng-Shuang], Hu, R.[Rui], Bai, M.[Min], Yumer, E.[Ersin], Urtasun, R.[Raquel],
UPSNet: A Unified Panoptic Segmentation Network,
CVPR19(8810-8818).
IEEE DOI 2002
BibRef

Li, Q.Z.[Qi-Zhu], Arnab, A.[Anurag], Torr, P.H.S.[Philip H. S.],
Weakly- and Semi-supervised Panoptic Segmentation,
ECCV18(XV: 106-124).
Springer DOI 1810
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Co-Segmentation, Cosegmentation .


Last update:Jan 29, 2023 at 20:54:24