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
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.[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
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],
Bulò, 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 .