Research map / Detection and segmentation / Open-set and few-shot detection
Semantic Image Matting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add semantic trimap (ablation) | Basic matting model · Semantic Image Matting Dataset | SAD: 32.04 -> 30.24 | Helped |
| add multi-class discriminator (ablation) | Basic + semantic trimap · Semantic Image Matting Dataset | SAD: 30.24 -> 29.84 | Helped |
| add gradient-related losses (ablation) | Basic + semantic trimap + multi-class discriminator · Semantic Image Matting Dataset | SAD: 29.84 -> 27.87 | Helped |
| use semantic classification of matting regions, semantic trimap, multi-class discriminator, and gradient-related losses | SIM · Composition-1K | SAD: 35.8 -> 28.0 | Helped |
| use semantic classification of matting regions, semantic trimap, multi-class discriminator, and gradient-related losses | SIM · Semantic Image Matting Dataset | SAD: 39.28 -> 27.87 | Helped |
Closest papers by meaning
- Referring Image Matting (CVPR 2023 · similarity 0.91)
- Training Matting Models Without Alpha Labels (AAAI 2025 · similarity 0.91)
- In-Context Matting (CVPR 2024 · similarity 0.90)
- Privileged Prior Information Distillation for Image Matting (AAAI 2024 · similarity 0.89)
- Unifying Automatic and Interactive Matting with Pretrained ViTs (CVPR 2024 · similarity 0.87)
- Revisiting Context Aggregation for Image Matting (ICML 2024 · similarity 0.87)
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