Are Visual-Language Models Effective in Action Recognition? A Comparative Study

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ID: 282442
2024
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Abstract
Current vision-language foundation models, such as CLIP, have recently shown significant improvement in performance across various downstream tasks. However, whether such foundation models significantly improve more complex fine-grained action recognition tasks is still an open question. To answer this question and better find out the future research direction on human behavior analysis in-the-wild, this paper provides a large-scale study and insight on current state-of-the-art vision foundation models by comparing their transfer ability onto zero-shot and frame-wise action recognition tasks. Extensive experiments are conducted on recent fine-grained, human-centric action recognition datasets (e.g., Toyota Smarthome, Penn Action, UAV-Human, TSU, Charades) including action classification and segmentation.
Reference Key
brémond2024are Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mahmoud Ali; Di Yang; François Brémond
Journal arXiv
Year 2024
DOI
DOI not found
URL
http://arxiv.org/abs/2410.17149v1
Keywords
cs.cv

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