Subtask Prior-Driven Optimized Mechanism on Joint Video Moment Retrieval and Highlight Detection

Joint video moment retrieval and highlight detection is an emerging and challenging research task. It requires the generation of robust joint task features to satisfy the demands of video moment retrieval and video highlight detection. Moreover, it involves the interaction of multiple modalities. Presently, methods typically focus on the design of distinct enhancement modules and the addition of supplementary input data to improve the solution for joint video moment retrieval and highlight detection. However, they overlook subtask interference during joint training. Joint task learning leverages the correlations and complementarities between tasks, yet it also introduces task interference arising from the differences between tasks. In order to address task interference, we proposes a subtask prior-driven optimized mechanism. The mechanism consists of two stages. In the free stage, we train subtask model to get subtask prior features. In the constrained stage, the joint task model is constrained by the subtask. Besides, we propose a cross adaptive-gated mechanism. It addresses the issue of information loss in cross-modal fusion and filters out redundant information by conducting cross-modal interaction during feature compression and an adaptive gating process. Extensive experimental results exhibit the effectiveness of the subtask prior-driven optimized mechanism and the cross adaptive-gated transformer in joint video moment retrieval and highlight detection.