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Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation

Suha Kwak 1, 2 Minsu Cho 1, 2 Ivan Laptev 1 
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.
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Submitted on : Thursday, January 5, 2017 - 12:14:18 AM
Last modification on : Thursday, March 17, 2022 - 10:08:40 AM
Long-term archiving on: : Thursday, April 6, 2017 - 12:16:43 PM


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  • HAL Id : hal-01242724, version 2



Suha Kwak, Minsu Cho, Ivan Laptev. Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation. CVPR 2016 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2016, Las Vegas, United States. ⟨hal-01242724v2⟩



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