A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus

Abstract

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.

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• The Kinetics Human Action Video Dataset
• Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
• VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
• Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction
• Learning the Best Pooling Strategy for Visual Semantic Embedding

Cited by in corpus (5)

• Temporal Sentence Grounding in Videos: A Survey and Future Directions
• QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
• A Survey on Temporal Sentence Grounding in Videos
• Video Moment Retrieval with Text Query Considering Many-to-Many Correspondence Using Potentially Relevant Pair
• Coarse to Fine: Video Retrieval before Moment Localization