The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
End-to-End Video Instance Segmentation with Transformers
Open accessAbstract
Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Recent methods typically develop sophisticated pipelines to tackle this task. Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches.Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks.Code is available at: https://git.io/VisTR
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Cited by
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Transformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595
ViViT: A Video Vision Transformer
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00676 · Cited by 2,767
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415
TrackFormer: Multi-Object Tracking with Transformers
Meinhardt, T., Kirillov, A., Leal-Taixe, L., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00864 · Cited by 1,081
“Queries are able to discriminate between features from the two frames by applying a temporal feature encoding as in [56].”
MethodTwins: Revisiting the Design of Spatial Attention in Vision Transformers
Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., & Shen, C. (2021) · arXiv · doi:10.48550/arxiv.2104.13840 · Cited by 817
Self-citationMOTR: End-to-End Multiple-Object Tracking with Transformer
Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., & Wei, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19812-0_38 · Cited by 752
Incorporating Convolution Designs into Visual Transformers
Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., & Wu, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00062 · Cited by 611
Neimark, D., Bar, O., Zohar, M., & Asselmann, D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00355 · Cited by 551
Conditional Positional Encodings for Vision Transformers
Chu, X., Tian, Z., Zhang, B., Wang, X., & Shen, C. (2021) · arXiv · doi:10.48550/arxiv.2102.10882 · Cited by 489
Self-citationSemantic segmentation using Vision Transformers: A survey
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Meng, L., Li, H., Chen, B.-C., Lan, S., Wu, Z., Jiang, Y.-G., & Lim, S.-N. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01199 · Cited by 262
All Tokens Matter: Token Labeling for Training Better Vision Transformers
Jiang, Z., Hou, Q., Yuan, L., Zhou, D., Shi, Y., Jin, X., Wang, A., & Feng, J. (2021) · arXiv · doi:10.48550/arxiv.2104.10858 · Cited by 158
Segmenting Transparent Objects in the Wild with Transformer
Xie, E., Wang, W., Wang, W., Sun, P., Xu, H., Liang, D., & Luo, P. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/165 · Cited by 111
Chasing Sparsity in Vision Transformers: An End-to-End Exploration
Chen, T., Cheng, Y., Gan, Z., Yuan, L., Zhang, L., & Wang, Z. (2021) · arXiv · doi:10.48550/arxiv.2106.04533 · Cited by 111
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
Zhang, C., Zhang, M., Zhang, S., Jin, D., Zhou, Q., Cai, Z., Zhao, H., Liu, X., & Liu, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00713 · Cited by 107
ASFormer: Transformer for Action Segmentation
Yi, F., Wen, H., & Jiang, T. (2021) · Proceedings of the British Machine Vision Conference 2021 · doi:10.5244/c.35.49 · Cited by 170
“Very recently, many researchers also show the potential of pure or hybrid Transformer models for many vision tasks, including image classification [4, 10, 40, 45, 50], action classification [1], segmentation [44, 46, 52], et al.”
BackgroundMethodWidth & Depth Pruning for Vision Transformers
Yu, F., Huang, K., Wang, M., Cheng, Y., Chu, W., & Cui, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20222 · Cited by 95
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space
Chavan, A., Shen, Z., Liu, Z., Liu, Z., Cheng, K.-T., & Xing, E. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00488 · Cited by 72
Zhu, M., Tang, Y., & Han, K. (2021) · arXiv · doi:10.48550/arxiv.2104.08500 · Cited by 63
Revisiting Monocular Satellite Pose Estimation With Transformer
Wang, Z., Zhang, Z., Sun, X., Li, Z., & Yu, Q. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3161605 · Cited by 59
Multimodal Remote Sensing Image Matching via Learning Features and Attention Mechanism
Zhang, Y., Lan, C., Zhang, H., Ma, G., & Li, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3348980 · Cited by 37
Wang, P., Zheng, W., Chen, T., & Wang, Z. (2022) · arXiv · doi:10.48550/arxiv.2203.05962 · Cited by 80
“, 2020), segmentation (Chen et al., 2021a; Wang et al., 2021), image generation (Parmar et al.”
Background
Metadata
- Authors
- Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., & Xia, H.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 8737-8746
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3171516518
- Cited by
- 679
- References
- 14
- FWCI
- 49.55
- Type
- conference paper
- Language
- en
- Open access
- green
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