8.6.1.1 Panoptic Segmentation

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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


Zhao, Y.M.[Yi-Ming], Zhang, X.[Xiao], Huang, X.[Xinming],
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 Lovsz 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.[Yunhang], Cao, L.[Liujuan], Chen, Z.W.[Zhi-Wei], Lian, F.[Feihong], 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

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.[Hengshuang], Qi, X.J.[Xiao-Juan], Wang, L.[Liwei], Li, Z.[Zeming], 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

Aygn, M.[Mehmet], Oep, A.[Aljoa], Weber, M.[Mark], Maximov, M.[Maxim], Stachniss, C.[Cyrill], Behley, J.[Jens], Leal-Taix, 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], Bul, 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.[Shijian],
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.[Hongsheng], 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

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.[Huanyu], 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 13, 2022 at 22:02:22