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Models/T2MDiffusion(DeepLabV2-ResNet101)

T2MDiffusion(DeepLabV2-ResNet101)

Reported on 6 benchmarks across 2 tasks · 1 paper

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

Medical3 results

Semantic SegmentationonCOCO 2014 val
mIoU· uses extra data· 2023-09-08
45.7
best: 56.8 (DHR (Swin-L, Mask2Former))
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models arXiv:2309.04109
Semantic SegmentationonPASCAL VOC 2012 val
Mean IoU· uses extra data· 2023-09-08
73.3
best: 83.4 (SemPLeS (Swin-L))
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models arXiv:2309.04109
Semantic SegmentationonPASCAL VOC 2012 test
Mean IoU· uses extra data· 2023-09-08
74.2
best: 82.9 (SemPLeS (Swin-L))
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models arXiv:2309.04109

Audio3 results

10-shot image generationonCOCO 2014 val
mIoU· uses extra data· 2023-09-08
45.7
best: 56.8 (DHR (Swin-L, Mask2Former))
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models arXiv:2309.04109
10-shot image generationonPASCAL VOC 2012 val
Mean IoU· uses extra data· 2023-09-08
73.3
best: 83.4 (SemPLeS (Swin-L))
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models arXiv:2309.04109
10-shot image generationonPASCAL VOC 2012 test
Mean IoU· uses extra data· 2023-09-08
74.2
best: 82.9 (SemPLeS (Swin-L))
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models arXiv:2309.04109