Research map / Detection and segmentation / Open-set and few-shot detection
Learning To Count Everything
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FamNet instead of MAML | Few-shot counting · FSC-147 test | MAE: 24.90 -> 22.08 | Helped |
| Use FamNet instead of the strongest detector baseline, Mask R-CNN | Counting categories in COCO subsets · Test-COCO | MAE: 35.56 -> 22.76 | Helped |
| Train FamNet with CARPK data rather than use the FSC-147-pretrained model | FamNet · CARPK | MAE: 28.84 -> 18.19 | Helped |
| Increase exemplars from 1 to 3 (ablation) | FamNet · FSC-147 val | MAE: 26.55 -> 23.75 | Helped |
| Add multi-scale image features (ablation) | FamNet · FSC-147 val | MAE: 32.70 -> 27.80 | Helped |
| Add multi-scale exemplar features (ablation) | FamNet with multi-scale image features · FSC-147 val | MAE: 27.80 -> 24.32 | Helped |
| Add test-time adaptation (ablation) | FamNet with multi-scale image and exemplar features · FSC-147 val | MAE: 24.32 -> 23.75 | Helped |
Closest papers by meaning
- Learning to Count without Annotations (CVPR 2024 · similarity 0.90)
- VLCounter: Text-Aware Visual Representation for Zero-Shot Object Counting (AAAI 2024 · similarity 0.88)
- DAVE - A Detect-and-Verify Paradigm for Low-Shot Counting (CVPR 2024 · similarity 0.88)
- OmniCount: Multi-label Object Counting with Semantic-Geometric Priors (AAAI 2025 · similarity 0.88)
- Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting (AAAI 2024 · similarity 0.87)
- T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting (CVPR 2025 · similarity 0.87)
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