April 12, 2024, 4:46 a.m. | Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, XueQing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, Jiashi Feng

cs.CV updates on arXiv.org arxiv.org

arXiv:2301.07868v2 Announce Type: replace
Abstract: State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However, this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue, we present our pioneering work that enables parameter-efficient VTR using a pre-trained model, with only a small number of tunable parameters during training. Towards this goal, we propose a new method dubbed Multimodal Video …

abstract adapter applications art arxiv clip costs cs.cv datasets fine-tuning however issue multimodal per practical pre-trained model retrieval state storage storage costs text transfer transfer learning type video work

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