Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

Understand

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos.

• Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a pre-segmented video, which inevitably suffer from exhaustively enumerated candidates.
• To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy.
• Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training.