Research map / Label-efficient and robust learning / Domain adaptation and generalization
Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adapt the source-pretrained model using density isomorphism reconstruction on unlabeled video | UCF-QNRF pre-trained model · VENICE | MAE: 33.95 -> 11.23 | Helped |
| Adapt the source-pretrained model using density isomorphism reconstruction on unlabeled video | UCF-QNRF pre-trained model · UCSD | MAE: 7.96 -> 1.79 | Helped |
| Adapt the source-pretrained model using density isomorphism reconstruction on unlabeled video | UCF-QNRF pre-trained model · MALL | MAE: 4.27 -> 2.36 | Helped |
| Adapt the source-pretrained model using density isomorphism reconstruction on unlabeled video | UCF-QNRF pre-trained model · FDST | MAE: 4.77 -> 3.25 | Helped |
| Add reconstruction erroneousness modeling (ablation) | EDIREC-Net · VENICE | MAE: 14.66 -> 11.23 | Helped |
| Add reconstruction erroneousness modeling (ablation) | EDIREC-Net · UCSD | MAE: 2.22 -> 1.79 | Helped |
| Add reconstruction erroneousness modeling (ablation) | EDIREC-Net · MALL | MAE: 3.17 -> 2.36 | Helped |
| Add reconstruction erroneousness modeling (ablation) | EDIREC-Net · FDST | MAE: 3.97 -> 3.25 | Helped |
| Set adjacent-frame interval d above 5 (ablation) | EDIREC-Net · MALL validation | counting accuracy | Hurt |
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