Discriminative Image Generation with Diffusion Models for Zero-Shot Learning

• URL: http://arxiv.org/abs/2412.17219v2
• Date: Wed, 25 Dec 2024 11:57:49 GMT
• Title: Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
• Authors: Dingjie Fu, Wenjin Hou, Shiming Chen, Shuhuang Chen, Xinge You, Salman Khan, Fahad Shahbaz Khan,
• Abstract summary: We present DIG-ZSL, a novel Discriminative Image Generation framework for Zero-Shot Learning. We learn a discriminative class token (DCT) for each unseen class under the guidance of a pre-trained category discrimination model (CDM) In this paper, the extensive experiments and visualizations on four datasets show that our DIG-ZSL: (1) generates diverse and high-quality images, (2) outperforms previous state-of-the-art nonhuman-annotated semantic prototype-based methods by a large margin, and (3) achieves comparable or better performance than baselines that leverage human-annot
• Score: 53.44301001173801
• License:
• Abstract: Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature generation paradigm falls short of providing interpretable insights. In addition, existing approaches rely on semantic prototypes annotated by human experts, which exhibit a significant limitation in their scalability to generalized scenes. To overcome these deficiencies, a natural solution is to generate images for unseen classes using text prompts. To this end, We present DIG-ZSL, a novel Discriminative Image Generation framework for Zero-Shot Learning. Specifically, to ensure the generation of discriminative images for training an effective ZSL classifier, we learn a discriminative class token (DCT) for each unseen class under the guidance of a pre-trained category discrimination model (CDM). Harnessing DCTs, we can generate diverse and high-quality images, which serve as informative unseen samples for ZSL tasks. In this paper, the extensive experiments and visualizations on four datasets show that our DIG-ZSL: (1) generates diverse and high-quality images, (2) outperforms previous state-of-the-art nonhuman-annotated semantic prototype-based methods by a large margin, and (3) achieves comparable or better performance than baselines that leverage human-annotated semantic prototypes. The codes will be made available upon acceptance of the paper.

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