  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 improves over the
baseline on all 18/19 few-shot learning tasks and 8/8 out-of-distribution
generalization tasks. Finally, we perform several ablation studies and analyze
the impact of the pretrained object detector on the performance across this
suite of tasks.
