MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916623572533248 |
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| author | Tang, Haoran Cao, Meng Huang, Jinfa Liu, Ruyang Jin, Peng Li, Ge Liang, Xiaodan |
| author_facet | Tang, Haoran Cao, Meng Huang, Jinfa Liu, Ruyang Jin, Peng Li, Ge Liang, Xiaodan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10575 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval Tang, Haoran Cao, Meng Huang, Jinfa Liu, Ruyang Jin, Peng Li, Ge Liang, Xiaodan Computer Vision and Pattern Recognition 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. |
| title | MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.10575 |