TarViS: A Unified Approach for Target-based Video Segmentation Article Swipe
Ali Athar
,
Alexander Hermans
,
Jonathon Luiten
,
Deva Ramanan
,
Bastian Leibe
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.02657
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.02657
The general domain of video segmentation is currently fragmented into different tasks spanning multiple benchmarks. Despite rapid progress in the state-of-the-art, current methods are overwhelmingly task-specific and cannot conceptually generalize to other tasks. Inspired by recent approaches with multi-task capability, we propose TarViS: a novel, unified network architecture that can be applied to any task that requires segmenting a set of arbitrarily defined 'targets' in video. Our approach is flexible with respect to how tasks define these targets, since it models the latter as abstract 'queries' which are then used to predict pixel-precise target masks. A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining. To demonstrate its effectiveness, we apply TarViS to four different tasks, namely Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), Video Object Segmentation (VOS) and Point Exemplar-guided Tracking (PET). Our unified, jointly trained model achieves state-of-the-art performance on 5/7 benchmarks spanning these four tasks, and competitive performance on the remaining two. Code and model weights are available at: https://github.com/Ali2500/TarViS
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2301.02657
- https://arxiv.org/pdf/2301.02657
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313965091
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313965091Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2301.02657Digital Object Identifier
- Title
-
TarViS: A Unified Approach for Target-based Video SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-06Full publication date if available
- Authors
-
Ali Athar, Alexander Hermans, Jonathon Luiten, Deva Ramanan, Bastian LeibeList of authors in order
- Landing page
-
https://arxiv.org/abs/2301.02657Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2301.02657Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2301.02657Direct OA link when available
- Concepts
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Computer science, Segmentation, Artificial intelligence, Task (project management), Inference, Video tracking, Encoder, Computer vision, Pattern recognition (psychology), Object (grammar), Machine learning, Economics, Operating system, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.performance | 158, 168 |
| abstract_inverted_index.retraining. | 121 |
| abstract_inverted_index.Segmentation | 136, 140, 144 |
| abstract_inverted_index.architecture | 47 |
| abstract_inverted_index.conceptually | 28 |
| abstract_inverted_index.segmentation | 5 |
| abstract_inverted_index.pixel-precise | 92 |
| abstract_inverted_index.task-specific | 25, 120 |
| abstract_inverted_index.effectiveness, | 125 |
| abstract_inverted_index.overwhelmingly | 24 |
| abstract_inverted_index.Exemplar-guided | 148 |
| abstract_inverted_index.state-of-the-art | 157 |
| abstract_inverted_index.state-of-the-art, | 20 |
| abstract_inverted_index.https://github.com/Ali2500/TarViS | 180 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |