Discriminative Diffusion Models as Few-shot Vision and Language Learners
Authors: name: Xuehai He, name: Weixi Feng, name: Tsu-Jui Fu, name: Varun Jampani, name: Arjun Akula, name: Pradyumna Narayana, name: Sugato Basu, name: William Yang Wang, name: Xin Eric Wang

Diffusion models, such as Stable Diffusion, have shown incredible performance
on text-to-image generation. Since text-to-image generation often requires
models to generate visual concepts with fine-grained details and attributes
specified in text prompts, can we leverage the powerful representations learned
by pre-trained diffusion models for discriminative tasks such as image-text
matching? To answer this question, we propose a novel approach, Discriminative
Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models
into few-shot discriminative learners. Our approach uses the cross-attention
score of a Stable Diffusion model to capture the mutual influence between
visual and textual information and fine-tune the model via attention-based
prompt learning to perform image-text matching. By comparing DSD with
state-of-the-art methods on several benchmark datasets, we demonstrate the
potential of using pre-trained diffusion models for discriminative tasks with
superior results on few-shot image-text matching.