mlsanityData to 7 Oct 2026

ChatGPT-guided Semantics for Zero-shot Learning

arXiv 2310.11657 ↗First posted 18 Oct 2023cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 41 references that are in the map. First posted on arXiv 18 Oct 2023.

Builds on

30 of 41 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  7. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  8. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  9. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  10. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  11. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  12. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
18 more
  1. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  2. 2022Learning Video Representations from Large Language Modelscited by 204
  3. 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
  4. 2023AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotatorscited by 171
  5. 2014Evaluation of Output Embeddings for Fine-Grained Image Classificationcited by 166
  6. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  7. 2016Latent Embeddings for Zero-shot Classificationcited by 129
  8. 2016Synthesized Classifiers for Zero-Shot Learningcited by 113
  9. 2017A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoderscited by 64
  10. 2020Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learningcited by 64
  11. 2018Generative Dual Adversarial Network for Generalized Zero-shot Learningcited by 61
  12. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  13. 2023LLM-powered Data Augmentation for Enhanced Cross-lingual Performancecited by 51
  14. 2021Zero-Shot Learning on 3D Point Cloud Objects and Beyondcited by 38
  15. 2019Zero-shot Learning of 3D Point Cloud Objectscited by 34
  16. 2020Synthesizing the Unseen for Zero-shot Object Detectioncited by 32
  17. 2019Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objectscited by 24
  18. 2022Prompt-guided Scene Generation for 3D Zero-Shot Learningcited by 2

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Citing papers per year: 2025: 1.

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  1. 2025Semantic-Inductive Attribute Selection for Zero-Shot Learningcited by 0