ChatGPT-guided Semantics for Zero-shot Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2024, 00, pp. 418-425
- Issue Date:
- 2024-01-29
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ChatGPT-guided Semantics for Zero-shot Learning.pdf | Accepted version | 897.49 kB |
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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
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