MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval

• URL: http://arxiv.org/abs/2408.10575v1
• Date: Tue, 20 Aug 2024 06:30:37 GMT
• Title: MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
• Authors: Haoran Tang, Meng Cao, Jinfa Huang, Ruyang Liu, Peng Jin, Ge Li, Xiaodan Liang,
• Abstract summary: We propose MUSE, a multi-scale mamba with linear computational complexity for efficient cross-resolution modeling. Specifically, the multi-scale representations are generated by applying a feature pyramid on the last single-scale feature map. We employ the Mamba structure as an efficient multi-scale learner to jointly learn scale-wise representations.
• Score: 73.77101139365912
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-Video Retrieval (TVR) aims to align and associate relevant video content with corresponding natural language queries. Most existing TVR methods are based on large-scale pre-trained vision-language models (e.g., CLIP). However, due to the inherent plain structure of CLIP, few TVR methods explore the multi-scale representations which offer richer contextual information for a more thorough understanding. To this end, we propose MUSE, a multi-scale mamba with linear computational complexity for efficient cross-resolution modeling. Specifically, the multi-scale representations are generated by applying a feature pyramid on the last single-scale feature map. Then, we employ the Mamba structure as an efficient multi-scale learner to jointly learn scale-wise representations. Furthermore, we conduct comprehensive studies to investigate different model structures and designs. Extensive results on three popular benchmarks have validated the superiority of MUSE.

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