mlsanityData to 7 Oct 2026

Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization

arXiv 2101.09892 ↗First posted 25 Jan 2021cs.CVcited by 5

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Abstract (arXiv)

Although zero-shot learning (ZSL) has an inferential capability of recognizing new classes that have never been seen before, it always faces two fundamental challenges of the cross modality and crossdomain challenges. In order to alleviate these problems, we develop a generative network-based ZSL approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and Taxonomy Regularization (TR). In our approach, the semantic features are taken as inputs, and the output is the synthesized visual features generated from the corresponding semantic features. CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network. Extensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB and aPY) show that our approach is superior to these state-of-the-art methods in terms of ZSL image classification and retrieval.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 42 of its 72 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

42 of 72 references.

References · 42

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30 more
  1. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
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  9. 2017Multi-Label Zero-Shot Learning with Structured Knowledge Graphscited by 72
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  12. 2017Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networkscited by 66
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  14. 2018Generative Dual Adversarial Network for Generalized Zero-shot Learningcited by 61
  15. 2019Semantic-Guided Multi-Attention Localization for Zero-Shot Learningcited by 58
  16. 2015Transductive Multi-view Zero-Shot Learningcited by 53
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  25. 2018Attribute-Guided Network for Cross-Modal Zero-Shot Hashingcited by 10
  26. 2015Write a Classifier: Predicting Visual Classifiers from Unstructured Textcited by 9
  27. 2017Transductive Zero-Shot Learning with Adaptive Structural Embeddingcited by 7
  28. 2017Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classescited by 4
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  30. 2016Semi-supervised Zero-Shot Learning by a Clustering-based Approachcited by 1

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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.

5 papers.

  1. 2024Unseen No More: Unlocking the Potential of CLIP for Generative Zero-shot HOI Detectioncited by 11
  2. 2022Text2Model: Text-based Model Induction for Zero-shot Image Classificationcited by 1
  3. 2021Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learningcited by 0
  4. 2021Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classificationcited by 0
  5. 2023A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarioscited by 0