  Temporal action localization is an important and challenging task that aims
to locate temporal regions in real-world untrimmed videos where actions occur
and recognize their classes. It is widely acknowledged that video context is a
critical cue for video understanding, and exploiting the context has become an
important strategy to boost localization performance. However, previous
state-of-the-art methods focus more on exploring semantic context which
captures the feature similarity among frames or proposals, and neglect
positional context which is vital for temporal localization. In this paper, we
propose a temporal-position-sensitive context modeling approach to incorporate
both positional and semantic information for more precise action localization.
Specifically, we first augment feature representations with directed temporal
positional encoding, and then conduct attention-based information propagation,
in both frame-level and proposal-level. Consequently, the generated feature
representations are significantly empowered with the discriminative capability
of encoding the position-aware context information, and thus benefit boundary
detection and proposal evaluation. We achieve state-of-the-art performance on
both two challenging datasets, THUMOS-14 and ActivityNet-1.3, demonstrating the
effectiveness and generalization ability of our method.
