Representation learning from videos in-the-wild: An object-centric approach

@article{Romijnders2021RepresentationLF,
  title={Representation learning from videos in-the-wild: An object-centric approach},
  author={Rob Romijnders and Aravindh Mahendran and Michael Tschannen and Josip Djolonga and Marvin Ritter and Neil Houlsby and Mario Lucic},
  journal={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={177-187}
}
We propose a method to learn image representations from uncurated videos. We combine a supervised loss from off-the-shelf object detectors and self-supervised losses which naturally arise from the video-shot-frame-object hierarchy present in each video. We report competitive results on 19 transfer learning tasks of the Visual Task Adaptation Benchmark (VTAB), and on 8 out-of-distribution-generalization tasks, and discuss the benefits and shortcomings of the proposed approach. In particular, it… Expand
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