UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning

TL;DR

UniDiff is a unified multimodal model that combines discriminative and generative learning to improve vision-language tasks, especially on small datasets, by leveraging semantic alignment and consistency without altering the pre-trained architecture.

Contribution

The paper introduces UniDiff, a novel model integrating contrastive, generative, and consistency learning for enhanced multimodal understanding and synthesis during fine-tuning.

Findings

Significant improvements in vision-language retrieval accuracy.

Enhanced quality in text-to-image generation.

Effective mitigation of semantic collapse during fine-tuning.

Abstract

Recent advances in vision-language pre-training have enabled machines to perform better in multimodal object discrimination (e.g., image-text semantic alignment) and image synthesis (e.g., text-to-image generation). On the other hand, fine-tuning pre-trained models with discriminative or generative capabilities such as CLIP and Stable Diffusion on domain-specific datasets has shown to be effective in various tasks by adapting to specific domains. However, few studies have explored the possibility of learning both discriminative and generative capabilities and leveraging their synergistic effects to create a powerful and personalized multimodal model during fine-tuning. This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC). UniDiff effectively learns aligned semantics and mitigates the issue of semantic collapse during fine-tuning on small datasets by leveraging RSC on visual features from CLIP and diffusion models, without altering the pre-trained model's basic architecture. UniDiff demonstrates versatility in both multi-modal understanding and generative tasks. Experimental results on three datasets (Fashion-man, Fashion-woman, and E-commercial Product) showcase substantial enhancements in vision-language retrieval and text-to-image generation, illustrating the advantages of combining discriminative and generative fine-tuning. The proposed UniDiff model establishes a robust pipeline for personalized modeling and serves as a benchmark for future comparisons in the field.