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

Understand

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.