Moving Beyond More Views: Redundancy-Aware Ego-Exo Fusion for Proficiency Estimation
EgoExo proficiency estimation aims to assess action quality by integrating fine-grained motion cues from egocentric (1st-person) views with spatial context from multiple exocentric (3rd-person) views. Simply adding more exocentric views degrades EgoExo performance, as redundant or noisy perspectives dilute useful motion cues. Our analysis identifies two key causes: (1) Multiview redundancy - From the data perspective, certain views provide limited or noisy information, diluting discriminative cues; (2) Overfitting - From the feature perspective, conventional fusion increases representational complexity, causing the model to memorise view-specific patterns rather than learn generalisable r...
| Authors | Xu Dong, Wanqing Li, Anthony Adeyemi-Ejeye et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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