A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus
This work provides a more precise video search and navigation capability for users by localizing undefined segments in untrimmed videos, which is an incremental improvement over existing retrieval systems.
This paper addresses the problem of localizing specific moments within long, untrimmed videos based on text queries. The proposed HAMMER model encodes videos at both clip and frame levels, achieving new state-of-the-art performance on the ActivityNet Captions and TVR datasets.
Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation. Typical retrieval systems respond to a query with either a whole video or a pre-defined video segment, but it is challenging to localize undefined segments in untrimmed and unsegmented videos where exhaustively searching over all possible segments is intractable. The outstanding challenge is that the representation of a video must account for different levels of granularity in the temporal domain. To tackle this problem, we propose the HierArchical Multi-Modal EncodeR (HAMMER) that encodes a video at both the coarse-grained clip level and the fine-grained frame level to extract information at different scales based on multiple subtasks, namely, video retrieval, segment temporal localization, and masked language modeling. We conduct extensive experiments to evaluate our model on moment localization in video corpus on ActivityNet Captions and TVR datasets. Our approach outperforms the previous methods as well as strong baselines, establishing new state-of-the-art for this task.