Parameter-Efficient Multi-View Proficiency Estimation: From Discriminative Classification to Generative Feedback
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| Authors | Edoardo Bianchi & Antonio Liotta |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2605.03848 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
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.
Engineering Breakdown
Plain English
This paper tackles the problem of estimating how well someone performs a physical action (proficiency) rather than just recognizing what action they're doing. The authors present three complementary approaches: SkillFormer uses parameter-efficient multi-view fusion to combine video from different camera angles, PATS improves temporal sampling to capture the subtle timing of movements, and ProfVLM reframes the problem as language generation to produce both a proficiency score and expert-style feedback in a single pass.
Key Engineering Insight
The shift from discriminative classification (predicting a label) to generative feedback through language models (ProfVLM) is significant because it lets a single model output both structured proficiency scores and human-readable explanations. This parallels broader trends in ML where generation-based approaches provide more interpretable and actionable outputs than classification alone.
Why It Matters for Engineers
Proficiency estimation has immediate real-world applications in rehabilitation, coaching, and athletic talent identification where you need not just a score but actionable feedback explaining why performance is at that level. The parameter-efficient designs (SkillFormer, PATS) matter for production because they reduce model size and inference cost while handling multi-view video—a computationally expensive input that's impractical to run at scale with heavy models.
Research Context
Prior work on action recognition focused on what action is happening; proficiency estimation requires detecting subtle biomechanical differences in how it's performed. This paper advances the state by introducing three modular components—discriminative fusion, temporal sampling, and generative explanation—that each solve a different bottleneck in the pipeline, moving the field from binary classification toward explainable, multi-modal feedback systems that trainers and patients can actually use.
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