Towards Generative Class Prompt Learning for Fine-grained Visual Recognition

• URL: http://arxiv.org/abs/2409.01835v2
• Date: Sat, 7 Sep 2024 22:51:50 GMT
• Title: Towards Generative Class Prompt Learning for Fine-grained Visual Recognition
• Authors: Soumitri Chattopadhyay, Sanket Biswas, Emanuele Vivoli, Josep Lladós,
• Abstract summary: Generative Class Prompt Learning and Contrastive Multi-class Prompt Learning are presented. Generative Class Prompt Learning improves visio-linguistic synergy in class embeddings by conditioning on few-shot exemplars with learnable class prompts. CoMPLe builds on this foundation by introducing a contrastive learning component that encourages inter-class separation.
• Score: 5.633314115420456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-grained categorization. Moreover, foundational models trained on one domain do not generalize well on a different domain without fine-tuning. We attribute these to the limitations of the VLM's semantic representations and attempt to improve their fine-grained visual awareness using generative modeling. Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe). Utilizing text-to-image diffusion models, GCPL significantly improves the visio-linguistic synergy in class embeddings by conditioning on few-shot exemplars with learnable class prompts. CoMPLe builds on this foundation by introducing a contrastive learning component that encourages inter-class separation during the generative optimization process. Our empirical results demonstrate that such a generative class prompt learning approach substantially outperform existing methods, offering a better alternative to few shot image recognition challenges. The source code will be made available at: https://github.com/soumitri2001/GCPL.

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