title: Understanding Video Transformers via Universal Concept Discovery
summary: This paper studies the problem of concept-based interpretability of
  transformer representations for videos. Concretely, we seek to explain the
  decision-making process of video transformers based on high-level,
  spatiotemporal concepts that are automatically discovered. Prior research on
  concept-based interpretability has concentrated solely on image-level tasks.
  Comparatively, video models deal with the added temporal dimension, increasing
  complexity and posing challenges in identifying dynamic concepts over time. In
  this work, we systematically address these challenges by introducing the first
  Video Transformer Concept Discovery (VTCD) algorithm. To this end, we propose
  an efficient approach for unsupervised identification of units of video
  transformer representations - concepts, and ranking their importance to the
  output of a model. The resulting concepts are highly interpretable, revealing
  spatio-temporal reasoning mechanisms and object-centric representations in
  unstructured video models. Performing this analysis jointly over a diverse set
  of supervised and self-supervised representations, we discover that some of
  these mechanism are universal in video transformers. Finally, we demonstrate
  that VTCDcan be used to improve model performance for fine-grained tasks.
paper_authors._id: 65adefe8c14c3cf579f8306c
paper_authors.name: Matthew Kowal
paper_authors.status: claimed_verified
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paper_authors.fullname: MATTHEW KOWAL
paper_authors.type: user
paper_authors.user: MKowal2
paper_authors._id: 65adefe8c14c3cf579f8306d
paper_authors.name: Achal Dave
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paper_authors.fullname: Achal Dave
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paper_authors.user: achal-tri
paper_authors._id: 65adefe8c14c3cf579f8306e
paper_authors.name: Rares Ambrus
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paper_authors.fullname: Rares Ambrus
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paper_authors.user: raramb
paper_authors._id: 65adefe8c14c3cf579f8306f
paper_authors.name: Adrien Gaidon
paper_authors.status: admin_assigned
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paper_authors._id: 64af547e46a29208dafca2f3
paper_authors.avatarUrl: /avatars/daff86330d644319c10347d230ab2452.svg
paper_authors.fullname: Adrien Gaidon
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paper_authors.user: adriengaidon
paper_authors._id: 65adefe8c14c3cf579f83070
paper_authors.name: Konstantinos G. Derpanis
paper_authors._id: 65adefe8c14c3cf579f83071
paper_authors.name: Pavel Tokmakov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-22T21:11:20.632Z
paper_authors._id: 65aeab2201b6f61e24323339
paper_authors.avatarUrl: /avatars/2c119173c0871f59df108f9bb4471954.svg
paper_authors.fullname: Pavel Tokmakov
paper_authors.type: user
paper_authors.user: ptokmakov
paper_title: Understanding Video Transformers via Universal Concept Discovery
paper_summary: This paper studies the problem of concept-based interpretability of
  transformer representations for videos. Concretely, we seek to explain the
  decision-making process of video transformers based on high-level,
  spatiotemporal concepts that are automatically discovered. Prior research on
  concept-based interpretability has concentrated solely on image-level tasks.
  Comparatively, video models deal with the added temporal dimension, increasing
  complexity and posing challenges in identifying dynamic concepts over time. In
  this work, we systematically address these challenges by introducing the first
  Video Transformer Concept Discovery (VTCD) algorithm. To this end, we propose
  an efficient approach for unsupervised identification of units of video
  transformer representations - concepts, and ranking their importance to the
  output of a model. The resulting concepts are highly interpretable, revealing
  spatio-temporal reasoning mechanisms and object-centric representations in
  unstructured video models. Performing this analysis jointly over a diverse set
  of supervised and self-supervised representations, we discover that some of
  these mechanism are universal in video transformers. Finally, we demonstrate
  that VTCDcan be used to improve model performance for fine-grained tasks.
paper_ai_keywords: transformer representations concept-based interpretability decision-making process spatiotemporal concepts Video Transformer Concept Discovery (VTCD) unsupervised identification ranking spatio-temporal reasoning mechanisms object-centric representations fine-grained tasks