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Communication Dans Un Congrès Année : 2016

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

Résumé

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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Dates et versions

hal-01242724 , version 1 (14-12-2015)
hal-01242724 , version 2 (05-01-2017)

Identifiants

  • HAL Id : hal-01242724 , version 2

Citer

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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