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Shallow Feature Based Dense Attention Network for Crowd Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the shallow-feature attention module (AMG) (ablation) | SDANet · WorldExpo'10 | MAE: 12.89 -> 8.10 | Helped |
| Add the shallow-feature attention module (AMG) (ablation) | SDANet · WorldExpo'10 | MSE: 15.28 -> 12.90 | Helped |
| Add dense connections between feature layers (ablation) | SDANet · WorldExpo'10 | MAE: 10.14 -> 8.10 | Helped |
| Add dense connections between feature layers (ablation) | SDANet · WorldExpo'10 | MSE: 13.25 -> 12.90 | Helped |
| Add refinement layers and L_map for coarse-to-fine density estimation (ablation) | SDANet · WorldExpo'10 | MAE: 9.64 -> 8.10 | Helped |
| Add refinement layers and L_map for coarse-to-fine density estimation (ablation) | SDANet · WorldExpo'10 | MSE: 13.19 -> 12.90 | Helped |
Closest papers by meaning
- Attention Scaling for Crowd Counting (CVPR 2020 · similarity 0.92)
- Top-Down Feedback for Crowd Counting Convolutional Neural Network (AAAI 2018 · similarity 0.90)
- Context-Aware Crowd Counting (CVPR 2019 · similarity 0.90)
- Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR 2016 · similarity 0.90)
- Gramformer: Learning Crowd Counting via Graph-Modulated Transformer (AAAI 2024 · similarity 0.88)
- Single Domain Generalization for Crowd Counting (CVPR 2024 · similarity 0.87)
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