Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word-vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method.

翻译：零样本学习旨在对训练过程中未观测或未见过的目标进行分类，其核心依赖类别语义描述将知识从可见类迁移至不可见类。现有类别语义获取方法包括人工标注属性和基于语言模型（如Word2Vec）自动生成的词向量。然而属性标注成本高昂，自动词向量则存在噪声问题。针对这一挑战，我们探索利用大型语言模型ChatGPT增强零样本学习任务中的类别语义。ChatGPT可为每个类别生成包含相关属性与语义的文本描述，成为有效的语义来源。我们采用Word2Vec模型从ChatGPT生成的文本中提取词向量，并通过融合类别名称与ChatGPT描述的词嵌入来增强词向量表征。具体而言，我们利用ChatGPT为类别描述提供额外监督信息，最终提升零样本学习模型性能。在2D图像数据集（CUB、AwA）与3D点云数据集（ModelNet10、ModelNet40、ScanObjectNN）上的实验表明，所提方法有效改善了零样本学习效果。本工作通过应用ChatGPT增强类别语义并创新性地提出词向量融合方法，为零样本学习研究领域做出贡献。