  Self-supervised learning presents a remarkable performance to utilize
unlabeled data for various video tasks. In this paper, we focus on applying the
power of self-supervised methods to improve semi-supervised action proposal
generation. Particularly, we design an effective Self-supervised
Semi-supervised Temporal Action Proposal (SSTAP) framework. The SSTAP contains
two crucial branches, i.e., temporal-aware semi-supervised branch and
relation-aware self-supervised branch. The semi-supervised branch improves the
proposal model by introducing two temporal perturbations, i.e., temporal
feature shift and temporal feature flip, in the mean teacher framework. The
self-supervised branch defines two pretext tasks, including masked feature
reconstruction and clip-order prediction, to learn the relation of temporal
clues. By this means, SSTAP can better explore unlabeled videos, and improve
the discriminative abilities of learned action features. We extensively
evaluate the proposed SSTAP on THUMOS14 and ActivityNet v1.3 datasets. The
experimental results demonstrate that SSTAP significantly outperforms
state-of-the-art semi-supervised methods and even matches fully-supervised
methods. Code is available at https://github.com/wangxiang1230/SSTAP.
