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Creators/Authors contains: "Krahenbuhl, Philipp"

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  1. ; (, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
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  2. ; (, CVPR)
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  3. ; ; (, CVPR)
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  4. ; ; ; (, CVPR)
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  5. ; ; (, International Conference on Computer Vision (ICCV))
    Accepted Manuscript Full Text Available
  6. ; (, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    Our world offers a never-ending stream of visual stimuli, yet today’s vision systems only accurately recognize patterns within a few seconds. These systems understand the present, but fail to contextualize it in past or future events. In this paper, we study long-form video understanding. We introduce a framework for modeling long-form videos and develop evaluation protocols on large-scale datasets. We show that existing state-of-the-art short-term models are limited for long-form tasks. A novel object-centric transformer-based video recognition architecture performs significantly better on 7 diverse tasks. It also outperforms comparable state-of-the-art on the AVA dataset. 
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  7. ; ; ; ; ; (, CVPR)
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