UVO (Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation)
Introduced by Wang et al. in Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation
UVO is a new benchmark for open-world class-agnostic object segmentation in videos. Besides shifting the problem focus to the open-world setup, UVO is significantly larger, providing approximately 8 times more videos compared with DAVIS, and 7 times more mask (instance) annotations per video compared with YouTube-VOS and YouTube-VIS. UVO is also more challenging as it includes many videos with crowded scenes and complex background motions. Some highlights of the dataset include:
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High quality instance masks densely annotated at 30 fps on 1024 YouTube videos and 1fps on 10337 videos from Kinetics dataset
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Open-world: annotating all objects in each video, 13.5 objects per video on average
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Diverse object categories: 57% of objects are not covered by COCO categories
Benchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
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UVO
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GLEE-Pro
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UVO
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CutLER
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UVO
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CutLER
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Papers
| Paper | Code | Results | Date | Stars |
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