PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill
  Assessment
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.
68429955c49e8ad3f997b24a
Edoardo Bianchi
extracted_pending
2025-06-06T07:31:34.170Z
622dc11fe27c88667db093fc
https://cdn-avatars.huggingface.co/v1/production/uploads/1667052350862-622dc11fe27c88667db093fc.jpeg
Edoardo Bianchi
user
EdBianchi
68429955c49e8ad3f997b24b
Antonio Liotta
PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill
  Assessment
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.
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.
Proficiency-Aware Temporal Sampling PATS EgoExo4D benchmark SkillFormer temporal continuity temporal coherence fundamental movement patterns dynamic sports sequential skills