Diverse and Tailored Image Generation for Zero-shot Multi-label Classification (2404.03144v1)

Abstract: Recently, zero-shot multi-label classification has garnered considerable attention for its capacity to operate predictions on unseen labels without human annotations. Nevertheless, prevailing approaches often use seen classes as imperfect proxies for unseen ones, resulting in suboptimal performance. Drawing inspiration from the success of text-to-image generation models in producing realistic images, we propose an innovative solution: generating synthetic data to construct a training set explicitly tailored for proxyless training on unseen labels. Our approach introduces a novel image generation framework that produces multi-label synthetic images of unseen classes for classifier training. To enhance diversity in the generated images, we leverage a pre-trained LLM to generate diverse prompts. Employing a pre-trained multi-modal CLIP model as a discriminator, we assess whether the generated images accurately represent the target classes. This enables automatic filtering of inaccurately generated images, preserving classifier accuracy. To refine text prompts for more precise and effective multi-label object generation, we introduce a CLIP score-based discriminative loss to fine-tune the text encoder in the diffusion model. Additionally, to enhance visual features on the target task while maintaining the generalization of original features and mitigating catastrophic forgetting resulting from fine-tuning the entire visual encoder, we propose a feature fusion module inspired by transformer attention mechanisms. This module aids in capturing global dependencies between multiple objects more effectively. Extensive experimental results validate the effectiveness of our approach, demonstrating significant improvements over state-of-the-art methods.