Research map / Label-efficient and robust learning / Self-supervised and contrastive
Exploit Clues From Views: Self-Supervised and Regularized Learning for Multiview Object Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add multiview consistency regularization to stochastic view prototypes (ablation) | MVSPE · ModelNet unseen classes | NMI: 48.4 -> 51.1 | Helped |
| Add multiview consistency regularization to stochastic view prototypes (ablation) | MVSPE · ModelNet unseen classes | Recall@1: 92.4 -> 95.5 | Helped |
| Use stochastic view prototypes instead of fixed prototypes (ablation) | PE · ModelNet unseen classes | Recall@1: 91.2 -> 92.4 | Helped |
| Use stochastic view prototypes instead of fixed prototypes (ablation) | PE · ModelNet unseen classes | NMI: 48.2 -> 48.4 | Helped |
| Randomize view prototypes during training (ablation) | VISPE · Unseen classes | accuracy | Helped |
| Minimize posterior-distribution variation across prototype sets (ablation) | MVSPE · ModelNet unseen classes | NMI: 48.4 -> 51.1 | Helped |
| Train with triplet loss rather than VISPE | VGG16 embeddings · ModelNet unseen classes, 1 labeled image per class | accuracy: 43.1 -> 41.4 | Hurt |
Closest papers by meaning
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- Self-Supervised Representation Learning with Meta Comprehensive Regularization (AAAI 2024 · similarity 0.85)
- Multi-View Multi-Label Classification via View-Label Matching Selection (AAAI 2025 · similarity 0.84)
- Harnessing small projectors and multiple views for efficient vision pretraining (NeurIPS 2024 · similarity 0.84)
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