  Temporal action detection (TAD) aims to detect the semantic labels and
boundaries of action instances in untrimmed videos. Current mainstream
approaches are multi-step solutions, which fall short in efficiency and
flexibility. In this paper, we propose a unified network for TAD, termed
Faster-TAD, by re-purposing a Faster-RCNN like architecture. To tackle the
unique difficulty in TAD, we make important improvements over the original
framework. We propose a new Context-Adaptive Proposal Module and an innovative
Fake-Proposal Generation Block. What's more, we use atomic action features to
improve the performance. Faster-TAD simplifies the pipeline of TAD and gets
remarkable performance on lots of benchmarks, i.e., ActivityNet-1.3 (40.01%
mAP), HACS Segments (38.39% mAP), SoccerNet-Action Spotting (54.09% mAP). It
outperforms existing single-network detector by a large margin.
