LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localization
The method uses a Large Language Model (LLM) to expand original language queries into detailed textual descriptions of action start and end boundaries. It then models boundary probabilities by comparing video features with these expanded queries using semantic similarity and temporal distance. This combination improves action localization accuracy by capturing fine-grained temporal details and handling uncertainty better during training.