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

• URL: http://arxiv.org/abs/2405.02155v1
• Date: Fri, 3 May 2024 15:02:41 GMT
• Title: Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification
• Authors: Siqi Yin, Lifan Jiang,
• Abstract summary: This paper introduces a novel framework for zero-shot learning (ZSL) to recognize new categories that are unseen during training. We propose three strategies to enhance the model's performance to handle ZSL.
• Score: 1.7265013728931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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; 2) Integrating the results of text-image alignment and image-image alignment from CLIP, along with the image-image alignment results from DINO, to achieve more accurate predictions; 3) Introducing an adaptive weighting mechanism based on confidence levels to aggregate the outcomes from different prediction methods. Experimental results on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet, demonstrate that our model can significantly improve classification accuracy compared to single-model approaches, achieving AUROC scores above 96% across all test datasets, and notably surpassing 99% on the CIFAR-10 dataset.

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