Back to Search Start Over

Improving Zero-Shot Generalization for CLIP with Synthesized Prompts

Authors :
Wang, Zhengbo
Liang, Jian
He, Ran
Xu, Nan
Wang, Zilei
Tan, Tieniu
Publication Year :
2023

Abstract

With the growing interest in pretrained vision-language models like CLIP, recent research has focused on adapting these models to downstream tasks. Despite achieving promising results, most existing methods require labeled data for all classes, which may not hold in real-world applications due to the long tail and Zipf's law. For example, some classes may lack labeled data entirely, such as emerging concepts. To address this problem, we propose a plug-and-play generative approach called \textbf{S}ynt\textbf{H}es\textbf{I}zed \textbf{P}rompts~(\textbf{SHIP}) to improve existing fine-tuning methods. Specifically, we follow variational autoencoders to introduce a generator that reconstructs the visual features by inputting the synthesized prompts and the corresponding class names to the textual encoder of CLIP. In this manner, we easily obtain the synthesized features for the remaining label-only classes. Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features. Extensive experiments on base-to-new generalization, cross-dataset transfer learning, and generalized zero-shot learning demonstrate the superiority of our approach. The code is available at \url{https://github.com/mrflogs/SHIP}.<br />Comment: Accepted by ICCV 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2307.07397
Document Type :
Working Paper