Research map / Label-efficient and robust learning / Domain adaptation and generalization
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use multi-mutual consistency transfer subspace learning instead of LTS | Human motion segmentation · Keck (source MAD) | ACC: 0.5509 -> 0.6010 | Helped |
| Use multi-mutual consistency transfer subspace learning instead of LTS | Human motion segmentation · MAD (source UT) | ACC: 0.5980 -> 0.6163 | Helped |
| Use multi-mutual consistency transfer subspace learning instead of LTS | Human motion segmentation · Weiz (source Keck) | NMI: 0.8599 -> 0.8371 | Hurt |
| Use multi-mutual consistency transfer subspace learning instead of LTS | Human motion segmentation · UT (source Weiz) | ACC: 0.6296 -> 0.6463 | Helped |
| Fuse subspace representations from multiple layers rather than use only the first or last layer (ablation) | Proposed method · Keck | NMI | Helped |
| Increase the number of source actions (ablation) | Proposed method · Keck | NMI | Helped |
| Increase the proportion of frames per source action (ablation) | Proposed method · Keck | NMI | Helped |
| Vary the consistency, temporal-preservation, and nuclear-norm regularization parameters (ablation) | Proposed method · Keck | NMI | Mixed |
Closest papers by meaning
- Learning Segmentation from Point Trajectories (NeurIPS 2024 · similarity 0.85)
- Learned Trajectory Embedding for Subspace Clustering (CVPR 2024 · similarity 0.83)
- H-MoRe: Learning Human-centric Motion Representation for Action Analysis (CVPR 2025 · similarity 0.83)
- Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation (CVPR 2024 · similarity 0.83)
- Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds (ICML 2024 · similarity 0.83)
- Unsupervised Action Segmentation via Fast Learning of Semantically Consistent Actoms (AAAI 2024 · similarity 0.83)
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