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This research strives for natural language moment retrieval in long,untrimmed video streams. The problem is not trivial especially when a videocontains multiple moments of interests and the language describes complextemporal dependencies, which often happens in real scenarios. We identify twocrucial challenges: semantic misalignment and structural misalignment. However,existing approaches treat different moments separately and do not explicitlymodel complex moment-wise temporal relations. In this paper, we present MomentAlignment Network (MAN), a novel framework that unifies the candidate momentencoding and temporal structural reasoning in a single-shot feed-forwardnetwork. MAN naturally assigns candidate moment representations aligned withlanguage semantics over different temporal locations and scales. Mostimportantly, we propose to explicitly model moment-wise temporal relations as astructured graph and devise an iterative graph adjustment network to jointlylearn the best structure in an end-to-end manner. We evaluate the proposedapproach on two challenging public benchmarks DiDeMo and Charades-STA, whereour MAN significantly outperforms the state-of-the-art by a large margin.

MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment