LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localization

The paper introduces a method that uses large language models (LLMs) to expand language queries with detailed textual descriptions of action start and end boundaries, providing richer cues for video action localization. It also proposes a training strategy that models boundary probability scores by combining semantic similarity between frames and the expanded query with temporal distance to annotated boundaries, thereby offering more consistent supervision. The approach is model‑agnostic and can be integrated into existing language‑driven action localization systems, with experiments on multiple datasets showing its effectiveness.

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