Abstract Summary
Video corpus moment retrieval is a challenging task that requires locating a specific moment from a large corpus of untrimmed videos using a natural language query. Existing methods typically rely on frame-level retrieval, which ranks videos by the maximum similarity between the query and individual frames. However, such approaches often overlook the semantic structure underlying consecutive frames, specifically, the concept of "events" which is fundamental to human video comprehension. To address this limitation, we propose EventFormer, a novel model that explicitly treats events as fundamental units for video retrieval. Our approach constructs event representations by first grouping consecutive, visually similar frames into coherent events via an event reasoning module, and then hierarchically encoding information at both the frame and event levels. Additionally, we introduce an anchor multi-head self-attention mechanism to enhance the modeling of local dependencies within the Transformer. Extensive experiments on three benchmark datasets (TVR, ANetCaps, and DiDeMo) demonstrate that EventFormer achieves state-of-the-art performance both in effectiveness and efficiency. The code for this work will be available on GitHub.
