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Video Panoptic Segmentation (VPS) aims to generate coherent panoptic segmentation and track the identities of all pixels across video frames.
J. Luiten, I. E. Zulfikar, and B. Leibe, “UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2020, pp. 2000–2009
2009
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” in Proceedings of the International Conference on 3D Vision (3DV) . IEEE, 2016, pp. 565–571
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , Oct 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” 2018
2018
Earlier work this paper cites.
Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, and R. Urtasun, “UPSNet: A Unified Panoptic Segmentation Network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 8818–8826
2019
Earlier work this paper cites.
L. Yang, Y. Fan, and N. Xu, “Video Instance Segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 5188–5197
2019
Earlier work this paper cites.
C. Ventura, M. Bellver, A. Girbau, A. Salvador, F. Marques, and X. Giro-i Nieto, “RVOS: End-to-End Recurrent Network for Video Object Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 5277–5286
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
X. Wang, A. Jabri, and A. A. Efros, “Learning Correspondence from the Cycle-Consistency of Time,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Earlier work this paper cites.
D. Kim, S. Woo, J.-Y. Lee, and I. S. Kweon, “Video Panoptic Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Earlier work this paper cites.
X. Zhou, V. Koltun, and P. Krähenbühl, “Tracking Objects as Points,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 474–490
2020
Earlier work this paper cites.
Z. Teed and J. Deng, “RAFT: Recurrent All-Pairs Field Transforms for Optical Flow,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 402–419
2020
Earlier work this paper cites.
L.-C. Chen, R. G. Lopes, B. Cheng, M. D. Collins, E. D. Cubuk, B. Zoph, H. Adam, and J. Shlens, “Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 695–714
2020
Earlier work this paper cites.
Y. Liu, C. Shen, C. Yu, and J. Wang, “Efficient Semantic Video Segmentation with Per-frame Inference,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 352–368
2020
Earlier work this paper cites.
Z. Xu, W. Zhang, X. Tan, W. Yang, H. Huang, S. Wen, E. Ding, and L. Huang, “Segment as Points for Efficient Online Multi-Object Tracking and Segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 264–281
2020
Earlier work this paper cites.
Y. Li, N. Xu, J. Peng, J. See, and W. Lin, “Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 1218–1228, 2020
2020
Cited alongside, same era.
L. Porzi, M. Hofinger, I. Ruiz, J. Serrat, S. R. Bulo, and P. Kontschieder, “Learning Multi-Object Tracking and Segmentation from Automatic Annotations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 6846–6855
2020
Cited alongside, same era.
B. McIntosh, K. Duarte, Y. S. Rawat, and M. Shah, “Visual-Textual Capsule Routing for Text-Based Video Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 9942–9951
2020
Cited alongside, same era.
A. Athar, S. Mahadevan, A. Osep, L. Leal-Taixé, and B. Leibe, “STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 158–177
Y. Heo, Y. J. Koh, and C.-S. Kim, “Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 7322–7330
2021
Later among the works it cites.
H. K. Cheng, Y.-W. Tai, and C.-K. Tang, “Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 5559–5568
2021
Later among the works it cites.
H. Xie, H. Yao, S. Zhou, S. Zhang, and W. Sun, “Efficient Regional Memory Network for Video Object Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 1286–1295
2021
Later among the works it cites.
B. Duke, A. Ahmed, C. Wolf, P. Aarabi, and G. W. Taylor, “SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 5912–5921
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2020
Cited alongside, same era.
G. Bertasius and L. Torresani, “Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 9739–9748
2020
Cited alongside, same era.
J. Cao, R. M. Anwer, H. Cholakkal, F. S. Khan, Y. Pang, and L. Shao, “SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
H. Yu, W. Ye, Y. Feng, H. Bao, and G. Zhang, “Learning Bipartite Graph Matching for Robust Visual Localization,” in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) . IEEE, 2020, pp. 146–155
2020
Cited alongside, same era.
Q. Wang, X. Zhou, B. Hariharan, and N. Snavely, “Learning Feature Descriptors using Camera Pose Supervision,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020
2020
Cited alongside, same era.
S. Woo, D. Kim, J.-Y. Lee, and I. S. Kweon, “Learning to Associate Every Segment for Video Panoptic Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 2705–2714
2021
Cited alongside, same era.
Y. Li, H. Zhao, X. Qi, L. Wang, Z. Li, J. Sun, and J. Jia, “Fully Convolutional Networks for Panoptic Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 214–223
2021
Cited alongside, same era.
W. Ye, H. Li, T. Zhang, X. Zhou, H. Bao, and G. Zhang, “SuperPlane: 3D Plane Detection and Description from a Single Image,” in 2021 IEEE Virtual Reality and 3D User Interfaces (VR) , 2021, pp. 207–215
2021
Cited alongside, same era.
2021
Later among the works it cites.
S. Ren, W. Liu, Y. Liu, H. Chen, G. Han, and S. He, “Reciprocal Transformations for Unsupervised Video Object Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 15 455–15 464
2021
Later among the works it cites.
Y. Yang, B. Lai, and S. Soatto, “DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 2826–2836
2021
Later among the works it cites.
T. Hui, S. Huang, S. Liu, Z. Ding, G. Li, W. Wang, J. Han, and F. Wang, “Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 4187–4196
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Zhou, J. Li, X. Li, and L. Shao, “Target-aware object discovery and association for unsupervised video multi-object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 6985–6994
2021
Later among the works it cites.
Y. Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, and H. Xia, “End-to-End Video Instance Segmentation with Transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Later among the works it cites.
X. Ying, X. Li, and M. C. Chuah, “SRNet: Spatial Relation Network for Efficient Single-stage Instance Segmentation in Videos,” in Proceedings of the 29th ACM International Conference on Multimedia (ACM MM) , 2021, pp. 347–356
2021
Later among the works it cites.
H. Lin, R. Wu, S. Liu, J. Lu, and J. Jia, “Video Instance Segmentation with a Propose-Reduce Paradigm,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 1739–1748
2021
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S. Qiao, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 3997–4008
2021
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M. Weber, J. Xie, M. Collins, Y. Zhu, P. Voigtlaender, H. Adam, B. Green, A. Geiger, B. Leibe, D. Cremers, A. Osep, L. Leal-Taixe, and L.-C. Chen, “STEP: Segmenting and Tracking Every Pixel,” in Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks , 2021
2021
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J. Pang, L. Qiu, X. Li, H. Chen, Q. Li, T. Darrell, and F. Yu, “Quasi-Dense Similarity Learning for Multiple Object Tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 164–173
2021
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