Research map / Label-efficient and robust learning / Recognition and long-tail
Almost Unsupervised Learning for Dense Crowd Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add GWTA sparsity to stacked autoencoder pretraining (ablation) | GWTA-CCNN · ShanghaiTech Part A | MAE: 193.0 -> 154.7 | Helped |
| add GWTA sparsity to stacked autoencoder pretraining (ablation) | GWTA-CCNN · UCF_CC_50 | MAE: 448.3 -> 433.7 | Helped |
| use tied decoder instead of untied large-filter decoder (ablation) | GWTA-CCNN · ShanghaiTech Part A | MAE: 195.6 -> 154.7 | Helped |
| use tied decoder instead of untied large-filter decoder (ablation) | GWTA-CCNN · UCF_CC_50 | MAE: 500.3 -> 433.7 | Helped |
| GWTA unsupervised pretraining instead of self-supervised colorization/inpainting | CCNN · ShanghaiTech Part A | MAE: 166.3 -> 154.7 | Helped |
| GWTA unsupervised pretraining instead of full supervision | CCNN · ShanghaiTech Part A | MAE: 124.6 -> 154.7 | Hurt |
| GWTA pretraining with very few labeled images | CCNN · ShanghaiTech Part A | MAE | Mixed |
Closest papers by meaning
- Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR 2018 · similarity 0.91)
- Divide and Grow: Capturing Huge Diversity in Crowd Images With Incrementally Growing CNN (CVPR 2018 · similarity 0.90)
- Leveraging Self-Supervision for Cross-Domain Crowd Counting (CVPR 2022 · similarity 0.90)
- Top-Down Feedback for Crowd Counting Convolutional Neural Network (AAAI 2018 · similarity 0.90)
- Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (CVPR 2019 · similarity 0.90)
- Gramformer: Learning Crowd Counting via Graph-Modulated Transformer (AAAI 2024 · similarity 0.88)
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