mlsanityData to 7 Oct 2026

Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions

arXiv 2103.09669 ↗First posted 17 Mar 2021cs.CVcited by 21

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Abstract (arXiv)

We study the impact of using rich and diverse textual descriptions of classes for zero-shot learning (ZSL) on ImageNet. We create a new dataset ImageNet-Wiki that matches each ImageNet class to its corresponding Wikipedia article. We show that merely employing these Wikipedia articles as class descriptions yields much higher ZSL performance than prior works. Even a simple model using this type of auxiliary data outperforms state-of-the-art models that rely on standard features of word embedding encodings of class names. These results highlight the usefulness and importance of textual descriptions for ZSL, as well as the relative importance of auxiliary data type compared to algorithmic progress. Our experimental results also show that standard zero-shot learning approaches generalize poorly across categories of classes.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 17 Mar 2021.

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17 of 25 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  7. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  8. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
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5 more
  1. 2015Yin and Yang: Balancing and Answering Binary Visual Questionscited by 109
  2. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
  3. 2017Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precisioncited by 46
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Citing papers per year: 2022: 8; 2023: 6; 2024: 4; 2025: 3.

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  1. 2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 310
  2. 2022What does a platypus look like? Generating customized prompts for zero-shot image classificationcited by 263
  3. 2024Why are Visually-Grounded Language Models Bad at Image Classification?cited by 101
  4. 2023Waffling around for Performance: Visual Classification with Random Words and Broad Conceptscited by 100
  5. 2023CHiLS: Zero-Shot Image Classification with Hierarchical Label Setscited by 79
  6. 2023Open-Set Image Tagging with Multi-Grained Text Supervisioncited by 67
  7. 2022Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Featurescited by 63
  8. 2022I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classificationcited by 54
  9. 2022I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classificationcited by 27
  10. 2023Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Imagescited by 13
  11. 2022Scaling up Multi-domain Semantic Segmentation with Sentence Embeddingscited by 6
  12. 2024Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot Learningcited by 4
9 more
  1. 2024Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIPcited by 4
  2. 2022SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalizationcited by 2
  3. 2023M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarioscited by 2
  4. 2025Coreset Selection via LLM-based Concept Bottleneckscited by 2
  5. 2025MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classificationcited by 2
  6. 2022Text2Model: Text-based Model Induction for Zero-shot Image Classificationcited by 1
  7. 2024LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classificationcited by 1
  8. 2025Towards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Modelscited by 1
  9. 2023Describe me an Aucklet: Generating Grounded Perceptual Category Descriptionscited by 0