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SkillMoV framework enhances multi-view video skill estimation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SkillMoV, a novel framework for estimating human proficiency from multi-view video. This parameter-efficient system utilizes a Mixture-of-View Projector (MoVP) that adapts the mixture-of-experts paradigm to different camera viewpoints. SkillMoV incorporates soft routing, cross-view attention, prototype anchoring, and gated projection to produce skill embeddings. When evaluated on the EgoExo4D dataset, SkillMoV achieved a 50.17% overall accuracy in the Exos setting, outperforming existing methods by 3.57 percentage points with a single, jointly trained model. AI

IMPACT Enhances automated skill assessment in diverse fields by improving multi-view video analysis.

RANK_REASON The cluster contains a research paper detailing a new framework and its evaluation on a dataset.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SkillMoV framework enhances multi-view video skill estimation

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Edoardo Bianchi, Antonio Liotta ·

    SkillMoV: Mixture-of-View Routing with Prototype-Conditioned Gating for Unified Multi-View Proficiency Estimation

    arXiv:2606.17615v1 Announce Type: cross Abstract: Estimating human proficiency from video is a key challenge for automated skill assessment, with applications in sports coaching, music pedagogy, surgical training, and workplace learning. Existing approaches often focus on individ…

  2. arXiv cs.CV TIER_1 English(EN) · Antonio Liotta ·

    SkillMoV: Mixture-of-View Routing with Prototype-Conditioned Gating for Unified Multi-View Proficiency Estimation

    Estimating human proficiency from video is a key challenge for automated skill assessment, with applications in sports coaching, music pedagogy, surgical training, and workplace learning. Existing approaches often focus on individual scenarios or rely on shared multi-view aggrega…

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