Research map / Label-efficient and robust learning / Self-supervised and contrastive
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed correspondence method instead of MBS | Self-supervised video correspondence on ResNet-18 · DAVIS2017 val | J &Fmean: 70.3 -> 70.5 | Helped |
| Use the proposed method instead of NRG | Self-supervised video correspondence · VIP | mIoU: 40.2 -> 40.8 | Helped |
| Use the proposed method instead of NRG | Self-supervised video correspondence · JHMDB | PCK@0.1: 61.4 -> 61.7 | Helped |
| Use the proposed method instead of CRW | Self-supervised video correspondence · JHMDB | PCK@0.2: 84.9 -> 82.6 | Hurt |
| Add hard-negative mining (ablation) | Proposed method · DAVIS2017 val | J &Fmean: 70.1 -> 70.5 | Helped |
| Add Bayesian model averaging (ablation) | Proposed method with hard-negative mining removed · DAVIS2017 val | J &Fmean: 69.4 -> 70.1 | Helped |
| Add self-cycle edges (ablation) | Proposed method without BMA and hard-negative mining · DAVIS2017 val | J &Fmean: 68.0 -> 69.4 | Helped |
| Add domain contrastive loss (ablation) | Proposed method without self-cycle edges, BMA, and hard-negative mining · DAVIS2017 val | J &Fmean: 67.6 -> 68.0 | Helped |
Closest papers by meaning
- Space-Time Correspondence as a Contrastive Random Walk (NeurIPS 2020 · similarity 0.92)
- Exposing the Self-Supervised Space-Time Correspondence Learning via Graph Kernels (AAAI 2023 · similarity 0.90)
- Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency (CVPR 2025 · similarity 0.88)
- Self-Supervised Spatial Correspondence Across Modalities (CVPR 2025 · similarity 0.85)
- Emergent Temporal Correspondences from Video Diffusion Transformers (NeurIPS 2025 · similarity 0.85)
- Decoupled Spatio-Temporal Consistency Learning for Self-Supervised Tracking (AAAI 2025 · similarity 0.84)
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