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.

我们研究了如何利用 ChatGPT 对零样本学习任务中的类语义进行增强，通过使用 ChatGPT 获得每个类的文本描述，结合类名和 ChatGPT 生成的描述生成的词嵌入来丰富词向量，并在各种 2D 图像和 3D 点云数据集上评估我们的方法，证明其提高了零样本学习的性能。