Moment is Important: Language-Based Video Moment Retrieval via Adversarial Learning

Abstract

The newly emerging language-based video moment retrieval task aims at retrieving a target video moment from an untrimmed video given a natural language as the query. It is more applicable in reality since it is able to accurately localize a specific video moment, as compared to traditional whole video retrieval. In this work, we propose a novel solution to thoroughly investigate the language-based video moment retrieval issue under the adversarial learning. The key of our solution is to formulate the language-based video moment retrieval task as an adversarial learning problem with two tightly connected components. Specifically, a reinforcement learning is employed as a generator to produce a set of possible video moments. Meanwhile, a multi-task learning is utilized as a discriminator, which integrates inter-modal and intra-modal in a unified framework by employing a sequential update strategy. Finally, the generator and the discriminator are mutually reinforced in the adversarial learning, which is able to jointly optimize the performance of both video moment ranking and video moment localization. Extensive experimental results on two challenging benchmarks, i.e., Charades-STA and TACoS datasets, have well demonstrated the effectiveness and rationality of our proposed solution. Meanwhile, on the larger and unbiased datasets, i.e., ActivityNet Captions and ActivityNet-CD, our proposed framework exhibits excellent robustness.