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Models/MTTR (Video-Swin-T)

MTTR (Video-Swin-T)

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision8 results

VideoonReVOS
F· 2021-11-29
25.9
best: 62.5 (VRS-HQ (Chat-UniVi-13B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
VideoonReVOS
J· 2021-11-29
25.1
best: 57.6 (VRS-HQ (Chat-UniVi-13B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
VideoonReVOS
J&F· 2021-11-29
25.5
best: 60 (VRS-HQ (Chat-UniVi-13B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
VideoonReVOS
R· 2021-11-29
5.6
best: 19.7 (VRS-HQ (Chat-UniVi-7B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
Video Object SegmentationonReVOS
F· 2021-11-29
25.9
best: 62.5 (VRS-HQ (Chat-UniVi-13B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
Video Object SegmentationonReVOS
J· 2021-11-29
25.1
best: 57.6 (VRS-HQ (Chat-UniVi-13B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
Video Object SegmentationonReVOS
J&F· 2021-11-29
25.5
best: 60 (VRS-HQ (Chat-UniVi-13B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821
Video Object SegmentationonReVOS
R· 2021-11-29
5.6
best: 19.7 (VRS-HQ (Chat-UniVi-7B))
SOTA
End-to-End Referring Video Object Segmentation with Multimodal Transformers arXiv:2111.14821