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Temporal action detection (TAD) aims to detect the semantic labels andboundaries of action instances in untrimmed videos. Current mainstreamapproaches are multi-step solutions, which fall short in efficiency andflexibility. In this paper, we propose a unified network for TAD, termedFaster-TAD, by re-purposing a Faster-RCNN like architecture. To tackle theunique difficulty in TAD, we make important improvements over the originalframework. We propose a new Context-Adaptive Proposal Module and an innovativeFake-Proposal Generation Block. What's more, we use atomic action features toimprove the performance. Faster-TAD simplifies the pipeline of TAD and getsremarkable performance on lots of benchmarks, i.e., ActivityNet-1.3 (40.01%mAP), HACS Segments (38.39% mAP), SoccerNet-Action Spotting (54.09% mAP). Itoutperforms existing single-network detector by a large margin.

Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network