  Self-supervised methods have significantly closed the gap with end-to-end
supervised learning for image classification. In the case of human action
videos, however, where both appearance and motion are significant factors of
variation, this gap remains significant. One of the key reasons for this is
that sampling pairs of similar video clips, a required step for many
self-supervised contrastive learning methods, is currently done conservatively
to avoid false positives. A typical assumption is that similar clips only occur
temporally close within a single video, leading to insufficient examples of
motion similarity. To mitigate this, we propose SLIC, a clustering-based
self-supervised contrastive learning method for human action videos. Our key
contribution is that we improve upon the traditional intra-video positive
sampling by using iterative clustering to group similar video instances. This
enables our method to leverage pseudo-labels from the cluster assignments to
sample harder positives and negatives. SLIC outperforms state-of-the-art video
retrieval baselines by +15.4% on top-1 recall on UCF101 and by +5.7% when
directly transferred to HMDB51. With end-to-end finetuning for action
classification, SLIC achieves 83.2% top-1 accuracy (+0.8%) on UCF101 and 54.5%
on HMDB51 (+1.6%). SLIC is also competitive with the state-of-the-art in action
classification after self-supervised pretraining on Kinetics400.
