SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrieval
Xuzheng Yu, Chen Jiang, Xingning Dong, Tian Gan, Ming Yang, Qingpei, Guo

TL;DR
SHE-Net leverages the syntax hierarchy of texts to improve the alignment and retrieval accuracy in text-video retrieval tasks by guiding visual focus and similarity computation.
Contribution
The paper introduces a novel approach that exploits text syntax hierarchy to enhance multi-modal interaction and alignment in text-video retrieval.
Findings
Outperforms existing methods on four public datasets.
Utilizes syntax hierarchy for fine-grained visual guidance.
Improves multi-modal similarity calculation accuracy.
Abstract
The user base of short video apps has experienced unprecedented growth in recent years, resulting in a significant demand for video content analysis. In particular, text-video retrieval, which aims to find the top matching videos given text descriptions from a vast video corpus, is an essential function, the primary challenge of which is to bridge the modality gap. Nevertheless, most existing approaches treat texts merely as discrete tokens and neglect their syntax structures. Moreover, the abundant spatial and temporal clues in videos are often underutilized due to the lack of interaction with text. To address these issues, we argue that using texts as guidance to focus on relevant temporal frames and spatial regions within videos is beneficial. In this paper, we propose a novel Syntax-Hierarchy-Enhanced text-video retrieval method (SHE-Net) that exploits the inherent semantic and…
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Taxonomy
TopicsNatural Language Processing Techniques · Video Analysis and Summarization · Multimodal Machine Learning Applications
MethodsBalanced Selection · Focus
