Many video analysis tasks require temporal localization thus detection of
content changes. However, most existing models developed for these tasks are
pre-trained on general video action classification tasks. This is because large
scale annotation of temporal boundaries in untrimmed videos is expensive.
Therefore no suitable datasets exist for temporal boundary-sensitive
pre-training. In this paper for the first time, we investigate model
pre-training for temporal localization by introducing a novel
boundary-sensitive pretext (BSP) task. Instead of relying on costly manual
annotations of temporal boundaries, we propose to synthesize temporal
boundaries in existing video action classification datasets. With the
synthesized boundaries, BSP can be simply conducted via classifying the
boundary types. This enables the learning of video representations that are
much more transferable to downstream temporal localization tasks. Extensive
experiments show that the proposed BSP is superior and complementary to the
existing action classification based pre-training counterpart, and achieves new
state-of-the-art performance on several temporal localization tasks.

本文首次探讨采用边界敏感预训练任务的方式进行时间定位的模型预训练，并在多个时间定位任务上实现了新的最先进性能，证明了该方法的有效性。