@inproceedings{
huang2024classification,
title={Classification Done Right for Vision-Language Pre-Training},
author={Zilong Huang and Qinghao Ye and Bingyi Kang and Jiashi Feng and Haoqi Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Hd2EOwKItm}
}
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
Classification Done Right for Vision-Language Pre-Training
Zilong Huang Qinghao Ye Bingyi Kang Jiashi Feng Haoqi Fan