Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification

arXiv:2405.02155v1 Announce Type: new
Abstract: This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by using a multi-model and multi-alignment integration method. Specifically, we propose three strategies to enhance the model's performance to handle ZSL: 1) Utilizing the extensive knowledge of ChatGPT and the powerful image generation capabilities of DALL-E to create reference images that can precisely describe unseen categories and classification boundaries, thereby alleviating the information bottleneck issue; …

abstract alignment arxiv classification confidence cs.cv framework image integration knowledge novel paper performance s performance strategies training type zero-shot