Understanding Video Transformers via Universal Concept Discovery
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.
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Matthew Kowal
claimed_verified
2024-01-22T21:11:22.648Z
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MATTHEW KOWAL
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MKowal2
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Achal Dave
admin_assigned
2024-01-22T13:22:12.469Z
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Achal Dave
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achal-tri
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Rares Ambrus
admin_assigned
2024-01-22T13:22:19.013Z
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Rares Ambrus
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raresambrus-lbm
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Adrien Gaidon
admin_assigned
2024-01-22T13:23:00.188Z
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Adrien Gaidon
user
adriengaidon
65adefe8c14c3cf579f83070
Konstantinos G. Derpanis
65adefe8c14c3cf579f83071
Pavel Tokmakov
claimed_verified
2024-01-22T21:11:20.632Z
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/avatars/2c119173c0871f59df108f9bb4471954.svg
Pavel Tokmakov
user
ptokmakov
Understanding Video Transformers via Universal Concept Discovery
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.
The study introduces a method to identify and rank spatiotemporal concepts in video transformer models, enhancing interpretability and performance on fine-grained tasks.
transformer representations concept-based interpretability video transformers spatiotemporal concepts unsupervised identification VTCD algorithm spatio-temporal reasoning mechanisms object-centric representations universal mechanisms fine-grained tasks