title: PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill
    Assessment
summary: Automated sports skill assessment requires capturing fundamental movement
  patterns that distinguish expert from novice performance, yet current video
  sampling methods disrupt the temporal continuity essential for proficiency
  evaluation. To this end, we introduce Proficiency-Aware Temporal Sampling
  (PATS), a novel sampling strategy that preserves complete fundamental movements
  within continuous temporal segments for multi-view skill assessment. PATS
  adaptively segments videos to ensure each analyzed portion contains full
  execution of critical performance components, repeating this process across
  multiple segments to maximize information coverage while maintaining temporal
  coherence. Evaluated on the EgoExo4D benchmark with SkillFormer, PATS surpasses
  the state-of-the-art accuracy across all viewing configurations (+0.65% to
  +3.05%) and delivers substantial gains in challenging domains (+26.22%
  bouldering, +2.39% music, +1.13% basketball). Systematic analysis reveals that
  PATS successfully adapts to diverse activity characteristics-from
  high-frequency sampling for dynamic sports to fine-grained segmentation for
  sequential skills-demonstrating its effectiveness as an adaptive approach to
  temporal sampling that advances automated skill assessment for real-world
  applications.
paper_authors._id: 68429955c49e8ad3f997b24a
paper_authors.name: Edoardo Bianchi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-06T07:31:34.170Z
paper_authors._id: 622dc11fe27c88667db093fc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1667052350862-622dc11fe27c88667db093fc.jpeg
paper_authors.fullname: Edoardo Bianchi
paper_authors.type: user
paper_authors.user: EdBianchi
paper_authors._id: 68429955c49e8ad3f997b24b
paper_authors.name: Antonio Liotta
paper_title: PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill
    Assessment
paper_summary: Automated sports skill assessment requires capturing fundamental movement
  patterns that distinguish expert from novice performance, yet current video
  sampling methods disrupt the temporal continuity essential for proficiency
  evaluation. To this end, we introduce Proficiency-Aware Temporal Sampling
  (PATS), a novel sampling strategy that preserves complete fundamental movements
  within continuous temporal segments for multi-view skill assessment. PATS
  adaptively segments videos to ensure each analyzed portion contains full
  execution of critical performance components, repeating this process across
  multiple segments to maximize information coverage while maintaining temporal
  coherence. Evaluated on the EgoExo4D benchmark with SkillFormer, PATS surpasses
  the state-of-the-art accuracy across all viewing configurations (+0.65% to
  +3.05%) and delivers substantial gains in challenging domains (+26.22%
  bouldering, +2.39% music, +1.13% basketball). Systematic analysis reveals that
  PATS successfully adapts to diverse activity characteristics-from
  high-frequency sampling for dynamic sports to fine-grained segmentation for
  sequential skills-demonstrating its effectiveness as an adaptive approach to
  temporal sampling that advances automated skill assessment for real-world
  applications.
paper_ai_summary: PATS is a novel temporal sampling method that enhances video analysis of athletic skills by ensuring complete movement patterns are captured, outperforming existing methods across various domains.
paper_ai_keywords: Proficiency-Aware Temporal Sampling PATS EgoExo4D benchmark SkillFormer temporal continuity temporal coherence fundamental movement patterns dynamic sports sequential skills