Boosting Video-Text Retrieval with Explicit High-Level Semantics

• URL: http://arxiv.org/abs/2208.04215v2
• Date: Tue, 9 Aug 2022 03:52:28 GMT
• Title: Boosting Video-Text Retrieval with Explicit High-Level Semantics
• Authors: Haoran Wang, Di Xu, Dongliang He, Fu Li, Zhong Ji, Jungong Han, Errui Ding
• Abstract summary: We propose a novel visual-linguistic aligning model named HiSE for VTR. It improves the cross-modal representation by incorporating explicit high-level semantics. Our method achieves the superior performance over state-of-the-art methods on three benchmark datasets.
• Score: 115.66219386097295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video). Existing methods typically employ completely heterogeneous visual-textual information to align video and text, whilst lacking the awareness of homogeneous high-level semantic information residing in both modalities. To fill this gap, in this work, we propose a novel visual-linguistic aligning model named HiSE for VTR, which improves the cross-modal representation by incorporating explicit high-level semantics. First, we explore the hierarchical property of explicit high-level semantics, and further decompose it into two levels, i.e. discrete semantics and holistic semantics. Specifically, for visual branch, we exploit an off-the-shelf semantic entity predictor to generate discrete high-level semantics. In parallel, a trained video captioning model is employed to output holistic high-level semantics. As for the textual modality, we parse the text into three parts including occurrence, action and entity. In particular, the occurrence corresponds to the holistic high-level semantics, meanwhile both action and entity represent the discrete ones. Then, different graph reasoning techniques are utilized to promote the interaction between holistic and discrete high-level semantics. Extensive experiments demonstrate that, with the aid of explicit high-level semantics, our method achieves the superior performance over state-of-the-art methods on three benchmark datasets, including MSR-VTT, MSVD and DiDeMo.

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