Region-aware Image-based Human Action Retrieval with Transformers

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ID: 282449
2024
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Abstract
Human action understanding is a fundamental and challenging task in computer vision. Although there exists tremendous research on this area, most works focus on action recognition, while action retrieval has received less attention. In this paper, we focus on the neglected but important task of image-based action retrieval which aims to find images that depict the same action as a query image. We establish benchmarks for this task and set up important baseline methods for fair comparison. We present an end-to-end model that learns rich action representations from three aspects: the anchored person, contextual regions, and the global image. A novel fusion transformer module is designed to model the relationships among different features and effectively fuse them into an action representation. Experiments on the Stanford-40 and PASCAL VOC 2012 Action datasets show that the proposed method significantly outperforms previous approaches for image-based action retrieval.
Reference Key
gui2024regionaware Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hongsong Wang; Jianhua Zhao; Jie Gui
Journal arXiv
Year 2024
DOI
DOI not found
URL
http://arxiv.org/abs/2407.09924v2
Keywords
cs.cv

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