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Models/RichSem (Focal-H + ImageNet as weakly-supervised extra data)

RichSem (Focal-H + ImageNet as weakly-supervised extra data)

Reported on 10 benchmarks across 5 tasks · 1 paper · 5 SOTA

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

Methodology8 results

3DonLVIS v1.0 val
box APr· uses extra data· 2023-10-18
61.2
best: 64 (Grounding DINO 1.5 Pro)
SOTA
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
2D ClassificationonLVIS v1.0 val
box APr· uses extra data· 2023-10-18
61.2
best: 64 (Grounding DINO 1.5 Pro)
SOTA
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
2D Object DetectiononLVIS v1.0 val
box APr· uses extra data· 2023-10-18
61.2
best: 64 (Grounding DINO 1.5 Pro)
SOTA
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
16konLVIS v1.0 val
box APr· uses extra data· 2023-10-18
61.2
best: 64 (Grounding DINO 1.5 Pro)
SOTA
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
3DonLVIS v1.0 val
box AP· uses extra data· 2023-10-18
61.2
best: 68 (Co-DETR (single-scale))
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
2D ClassificationonLVIS v1.0 val
box AP· uses extra data· 2023-10-18
61.2
best: 68 (Co-DETR (single-scale))
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
2D Object DetectiononLVIS v1.0 val
box AP· uses extra data· 2023-10-18
61.2
best: 68 (Co-DETR (single-scale))
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
16konLVIS v1.0 val
box AP· uses extra data· 2023-10-18
61.2
best: 68 (Co-DETR (single-scale))
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152

Computer Vision2 results

Object DetectiononLVIS v1.0 val
box APr· uses extra data· 2023-10-18
61.2
best: 64 (Grounding DINO 1.5 Pro)
SOTA
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152
Object DetectiononLVIS v1.0 val
box AP· uses extra data· 2023-10-18
61.2
best: 68 (Co-DETR (single-scale))
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection arXiv:2310.12152