Classification Done Right for Vision-Language Pre-Training

Zilong Huang, Qinghao Ye, Bingyi Kang, Jiashi Feng, Haoqi Fan

Keywords: Text Supervision, Visual Models, Pretraining, classification

Abstract: We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text encoder, SuperClass directly utilizes tokenized raw text as supervised classification labels, without the need for additional text filtering or selection. Due to the absence of the text encoding as contrastive target, SuperClass does not require a text encoder and does not need to maintain a large batch size as CLIP does. SuperClass demonstrated superior performance on various downstream tasks, including classic computer vision benchmarks and vision language downstream tasks. We further explored the scaling behavior of SuperClass on model size, training length, or data size, and reported encouraging results and comparisons to CLIP. https://github.com/x-cls/superclass

Primary Area: Other (please use sparingly, only use the keyword field for more details)

Submission Number: 537