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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Self-citationFEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
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Self-citation
Cited by
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Transformers in Vision: A Survey
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Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
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TrackFormer: Multi-Object Tracking with Transformers
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“Queries are able to discriminate between features from the two frames by applying a temporal feature encoding as in [56].”
Method
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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