MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
<|reference_start|>MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval: 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.<|reference_end|>
@article{tang2024muse:,
    title={MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval},
    author={Haoran Tang, Meng Cao, Jinfa Huang, Ruyang Liu, Peng Jin, Ge Li,
  Xiaodan Liang},
    journal={arXiv preprint arXiv:2408.10575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10575},
    primaryClass={cs.CV}
}