Parameter-Efficient Multi-View Proficiency Estimation: From Discriminative Classification to Generative Feedback

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parameter-efficient multi-view proficiency-estimation generative-feedback video-analysis

Summary

This paper introduces three parameter-efficient methods for multi-view proficiency estimation on the Ego-Exo4D dataset, shifting from discriminative classification to generative feedback. The proposed models achieve state-of-the-art accuracy with significantly fewer parameters and training epochs than video-transformer baselines.

Estimating how well a person performs an action, rather than which action is performed, is central to coaching, rehabilitation, and talent identification. This task is challenging because proficiency is encoded in subtle differences in timing, balance, body mechanics, and execution, often distributed across multiple views and short temporal events. We discuss three recent contributions to multi-view proficiency estimation on Ego-Exo4D. SkillFormer introduces a parameter-efficient discriminative architecture for selective multi-view fusion; PATS improves temporal sampling by preserving locally dense excerpts of fundamental movements; and ProfVLM reformulates proficiency estimation as conditional language generation, producing both a proficiency label and expert-style feedback through a gated cross-view projector and a compact language backbone. Together, these methods achieve state-of-the-art accuracy on Ego-Exo4D with up to 20x fewer trainable parameters and up to 3x fewer training epochs than video-transformer baselines, while moving from closed-set classification toward interpretable feedback generation. These results highlight a shift toward efficient, multi-view systems that combine selective fusion, proficiency-aware sampling, and actionable generative feedback.
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Source: https://huggingface.co/papers/2605.03848

Abstract

Three methods for multi-view proficiency estimation—SkillFormer, PATS, and ProfVLM—achieve state-of-the-art accuracy on Ego-Exo4D with reduced parameters and training epochs while enabling interpretable feedback generation.

Estimating how well a person performs an action, rather than which action is performed, is central to coaching, rehabilitation, and talent identification. This task is challenging because proficiency is encoded in subtle differences in timing, balance, body mechanics, and execution, often distributed across multiple views and short temporal events. We discuss three recent contributions to multi-viewproficiency estimationon Ego-Exo4D. SkillFormer introduces a parameter-efficient discriminative architecture for selectivemulti-view fusion; PATS improvestemporal samplingby preserving locally dense excerpts of fundamental movements; and ProfVLM reformulatesproficiency estimationasconditional language generation, producing both a proficiency label and expert-style feedback through agated cross-view projectorand a compactlanguage backbone. Together, these methods achieve state-of-the-art accuracy on Ego-Exo4D with up to 20x fewer trainable parameters and up to 3x fewer training epochs thanvideo-transformer baselines, while moving from closed-set classification toward interpretable feedback generation. These results highlight a shift toward efficient, multi-view systems that combine selective fusion, proficiency-aware sampling, and actionable generative feedback.

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