Research map / Detection and segmentation / Open-set and few-shot detection
Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add image reconstruction (ablation) | SAN · COCO-Stuff-3 | Acc. (%): 34.2 -> 80.3 | Helped |
| add batch-wise information (ablation) | SAN with image reconstruction and token matcher · COCO-Stuff-3 | Acc. (%): 79.2 -> 80.3 | Helped |
| add token matcher (ablation) | SAN with image reconstruction and batch-wise info · COCO-Stuff-3 | Acc. (%): 76.7 -> 80.3 | Helped |
| replace the 3x3 Conv pixel-wise encoder with ResNet18 (ablation) | SAN · COCO-Stuff-3 | Acc.: 80.3 -> 73.5 | Hurt |
| replace the 3x3 Conv pixel-wise encoder with UNet (ablation) | SAN · COCO-Stuff-3 | Acc.: 80.3 -> 71.9 | Hurt |
| use ViT-4 instead of ViT-6 as semantic-wise generator (ablation) | SAN · COCO-Stuff-3 | Acc.: 80.3 -> 79.9 | Hurt |
| replace the ViT semantic-wise generator with ResNet18 (ablation) | SAN · COCO-Stuff-3 | Acc.: 80.3 -> 77.8 | Hurt |
| replace the ViT semantic-wise generator with UNet (ablation) | SAN · COCO-Stuff-3 | Acc.: 80.3 -> 78.5 | Hurt |
| add cosine similarity loss to encourage semantic embedding uniformity (ablation) | SAN · not specified | Acc. | No clear effect |
Closest papers by meaning
- Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling (CVPR 2024 · similarity 0.88)
- Integrating Low-Level Visual Cues for Enhanced Unsupervised Semantic Segmentation (AAAI 2025 · similarity 0.88)
- Unsupervised Universal Image Segmentation (CVPR 2024 · similarity 0.88)
- Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure (NeurIPS 2024 · similarity 0.87)
- Label-Efficient Few-Shot Semantic Segmentation with Unsupervised Meta-Training (AAAI 2024 · similarity 0.86)
- Segment Anything without Supervision (NeurIPS 2024 · similarity 0.86)
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