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Models/CMT-DeepLab (single-scale)

CMT-DeepLab (single-scale)

Reported on 18 benchmarks across 3 tasks · 1 paper

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

Medical6 results

Semantic SegmentationonCOCO test-dev
PQ· 2022-06-17
55.7
best: 59.5 (Mask DINO (single scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Semantic SegmentationonCOCO test-dev
PQst· 2022-06-17
46.8
best: 58.9 (MaskConver (ResNet50, single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Semantic SegmentationonCOCO test-dev
PQth· 2022-06-17
61.6
best: 65.1 (Mask2Former (Swin-L))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Semantic SegmentationonCOCO minival
PQ· 2022-06-17
55.3
best: 61.2 (HyperSeg (Swin-B))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Semantic SegmentationonCOCO minival
PQst· 2022-06-17
46.6
best: 49.2 (OneFormer (InternImage-H,single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Semantic SegmentationonCOCO minival
PQth· 2022-06-17
61
best: 67.1 (OneFormer (InternImage-H,single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948

Audio6 results

10-shot image generationonCOCO test-dev
PQ· 2022-06-17
55.7
best: 59.5 (Mask DINO (single scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
10-shot image generationonCOCO test-dev
PQst· 2022-06-17
46.8
best: 58.9 (MaskConver (ResNet50, single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
10-shot image generationonCOCO test-dev
PQth· 2022-06-17
61.6
best: 65.1 (Mask2Former (Swin-L))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
10-shot image generationonCOCO minival
PQ· 2022-06-17
55.3
best: 61.2 (HyperSeg (Swin-B))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
10-shot image generationonCOCO minival
PQst· 2022-06-17
46.6
best: 49.2 (OneFormer (InternImage-H,single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
10-shot image generationonCOCO minival
PQth· 2022-06-17
61
best: 67.1 (OneFormer (InternImage-H,single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948

Computer Vision6 results

Panoptic SegmentationonCOCO test-dev
PQ· 2022-06-17
55.7
best: 59.5 (Mask DINO (single scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Panoptic SegmentationonCOCO test-dev
PQst· 2022-06-17
46.8
best: 58.9 (MaskConver (ResNet50, single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Panoptic SegmentationonCOCO test-dev
PQth· 2022-06-17
61.6
best: 65.1 (Mask2Former (Swin-L))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Panoptic SegmentationonCOCO minival
PQ· 2022-06-17
55.3
best: 61.2 (HyperSeg (Swin-B))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Panoptic SegmentationonCOCO minival
PQst· 2022-06-17
46.6
best: 49.2 (OneFormer (InternImage-H,single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948
Panoptic SegmentationonCOCO minival
PQth· 2022-06-17
61
best: 67.1 (OneFormer (InternImage-H,single-scale))
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation arXiv:2206.08948