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Article overview
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Hierarchical Explanations for Video Action Recognition | | Sadaf Gulshad
; Teng Long
; Nanne van Noord
; | | Date: |
1 Jan 2023 | | Abstract: | We propose Hierarchical ProtoPNet: an interpretable network that explains its
reasoning process by considering the hierarchical relationship between classes.
Different from previous methods that explain their reasoning process by
dissecting the input image and finding the prototypical parts responsible for
the classification, we propose to explain the reasoning process for video
action classification by dissecting the input video frames on multiple levels
of the class hierarchy. The explanations leverage the hierarchy to deal with
uncertainty, akin to human reasoning: When we observe water and human activity,
but no definitive action it can be recognized as the water sports parent class.
Only after observing a person swimming can we definitively refine it to the
swimming action. Experiments on ActivityNet and UCF-101 show performance
improvements while providing multi-level explanations. | | Source: | arXiv, 2301.00436 | | Services: | Forum | Review | PDF | Favorites | |
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