  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.
