Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

• URL: http://arxiv.org/abs/2111.03930v1
• Date: Sat, 6 Nov 2021 18:09:22 GMT
• Title: Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
• Authors: Renrui Zhang, Rongyao Fang, Peng Gao, Wei Zhang, Kunchang Li, Jifeng Dai, Yu Qiao, Hongsheng Li
• Abstract summary: We propose textbfTraining-Free CLtextbfIP-textbfAdapter (textbfTip-Adapter), which not only inherits CLIP's training-free advantage but also performs comparably or even better than CLIP-Adapter. We conduct extensive experiments of few-shot classification on ImageNet and other 10 datasets to demonstrate the superiority of proposed Tip-Adapter.
• Score: 78.62723847797382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations by using large-scale contrastive image-text pairs. It shows impressive performance on zero-shot knowledge transfer to downstream tasks. To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification. However, such a process still needs extra training and computational resources. In this paper, we propose \textbf{T}raining-Free CL\textbf{IP}-\textbf{Adapter} (\textbf{Tip-Adapter}), which not only inherits CLIP's training-free advantage but also performs comparably or even better than CLIP-Adapter. Tip-Adapter does not require any back propagation for training the adapter, but creates the weights by a key-value cache model constructed from the few-shot training set. In this non-parametric manner, Tip-Adapter acquires well-performed adapter weights without any training, which is both efficient and effective. Moreover, the performance of Tip-Adapter can be further boosted by fine-tuning such properly initialized adapter for only a few epochs with super-fast convergence speed. We conduct extensive experiments of few-shot classification on ImageNet and other 10 datasets to demonstrate the superiority of proposed Tip-Adapter. The code will be released at \url{https://github.com/gaopengcuhk/Tip-Adapter}.

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