  Temporal action proposal generation aims to estimate temporal intervals of
actions in untrimmed videos, which is a challenging yet important task in the
video understanding field. The proposals generated by current methods still
suffer from inaccurate temporal boundaries and inferior confidence used for
retrieval owing to the lack of efficient temporal modeling and effective
boundary context utilization. In this paper, we propose Temporal Context
Aggregation Network (TCANet) to generate high-quality action proposals through
"local and global" temporal context aggregation and complementary as well as
progressive boundary refinement. Specifically, we first design a Local-Global
Temporal Encoder (LGTE), which adopts the channel grouping strategy to
efficiently encode both "local and global" temporal inter-dependencies.
Furthermore, both the boundary and internal context of proposals are adopted
for frame-level and segment-level boundary regressions, respectively. Temporal
Boundary Regressor (TBR) is designed to combine these two regression
granularities in an end-to-end fashion, which achieves the precise boundaries
and reliable confidence of proposals through progressive refinement. Extensive
experiments are conducted on three challenging datasets: HACS,
ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high
precision and recall. By combining with the existing action classifier, TCANet
can obtain remarkable temporal action detection performance compared with other
methods. Not surprisingly, the proposed TCANet won the 1$^{st}$ place in the
CVPR 2020 - HACS challenge leaderboard on temporal action localization task.
