title: SkillFormer: Unified Multi-View Video Understanding for Proficiency
    Estimation
summary: Assessing human skill levels in complex activities is a challenging problem
  with applications in sports, rehabilitation, and training. In this work, we
  present SkillFormer, a parameter-efficient architecture for unified multi-view
  proficiency estimation from egocentric and exocentric videos. Building on the
  TimeSformer backbone, SkillFormer introduces a CrossViewFusion module that
  fuses view-specific features using multi-head cross-attention, learnable
  gating, and adaptive self-calibration. We leverage Low-Rank Adaptation to
  fine-tune only a small subset of parameters, significantly reducing training
  costs. In fact, when evaluated on the EgoExo4D dataset, SkillFormer achieves
  state-of-the-art accuracy in multi-view settings while demonstrating remarkable
  computational efficiency, using 4.5x fewer parameters and requiring 3.75x fewer
  training epochs than prior baselines. It excels in multiple structured tasks,
  confirming the value of multi-view integration for fine-grained skill
  assessment.
paper_authors._id: 68243bddd08d8e01109d5680
paper_authors.name: Edoardo Bianchi
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-14T06:45:06.471Z
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: 68243bddd08d8e01109d5681
paper_authors.name: Antonio Liotta
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:11:54.811Z
paper_authors._id: 66f2ab691e8b23ab0af8436e
paper_authors.avatarUrl: /avatars/161a26e9444a860128282e553a95641c.svg
paper_authors.fullname: Antonio Liotta
paper_authors.type: user
paper_authors.user: ucaclio
paper_title: SkillFormer: Unified Multi-View Video Understanding for Proficiency
    Estimation
paper_summary: Assessing human skill levels in complex activities is a challenging problem
  with applications in sports, rehabilitation, and training. In this work, we
  present SkillFormer, a parameter-efficient architecture for unified multi-view
  proficiency estimation from egocentric and exocentric videos. Building on the
  TimeSformer backbone, SkillFormer introduces a CrossViewFusion module that
  fuses view-specific features using multi-head cross-attention, learnable
  gating, and adaptive self-calibration. We leverage Low-Rank Adaptation to
  fine-tune only a small subset of parameters, significantly reducing training
  costs. In fact, when evaluated on the EgoExo4D dataset, SkillFormer achieves
  state-of-the-art accuracy in multi-view settings while demonstrating remarkable
  computational efficiency, using 4.5x fewer parameters and requiring 3.75x fewer
  training epochs than prior baselines. It excels in multiple structured tasks,
  confirming the value of multi-view integration for fine-grained skill
  assessment.
paper_ai_summary: SkillFormer, a parameter-efficient architecture, uses the TimeSformer backbone with a CrossViewFusion module to achieve state-of-the-art accuracy in multi-view skill assessment from egocentric and exocentric videos.
paper_ai_keywords: TimeSformer CrossViewFusion multi-head cross-attention learnable gating adaptive self-calibration Low-Rank Adaptation EgoExo4D dataset