Video-Text Retrieval by Supervised Sparse Multi-Grained Learning

• URL: http://arxiv.org/abs/2302.09473v2
• Date: Tue, 17 Oct 2023 22:01:00 GMT
• Title: Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
• Authors: Yimu Wang, Peng Shi
• Abstract summary: We present a novel multi-grained sparse learning framework, S3MA, to learn an sparse space shared between the video and the text for video-text retrieval. With the text data at hand, we learn and update the shared sparse space in a supervised manner using the proposed similarity and alignment losses. Benefiting from the learned shared sparse space and multi-grained similarities, experiments on several video-text retrieval benchmarks demonstrate the superiority of S3MA over existing methods.
• Score: 22.17732989393653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA, to learn an aligned sparse space shared between the video and the text for video-text retrieval. The shared sparse space is initialized with a finite number of sparse concepts, each of which refers to a number of words. With the text data at hand, we learn and update the shared sparse space in a supervised manner using the proposed similarity and alignment losses. Moreover, to enable multi-grained alignment, we incorporate frame representations for better modeling the video modality and calculating fine-grained and coarse-grained similarities. Benefiting from the learned shared sparse space and multi-grained similarities, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of S3MA over existing methods. Our code is available at https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.

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