mlsanityData to 7 Oct 2026

Region Semantically Aligned Network for Zero-Shot Learning

arXiv 2110.07130 ↗First posted 14 Oct 2021cs.CV cs.LGcited by 6

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Abstract (arXiv)

Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes. Previous methods focused on learning direct embeddings from global features to the semantic space in hope of knowledge transfer from seen classes to unseen classes. However, an unseen class shares local visual features with a set of seen classes and leveraging global visual features makes the knowledge transfer ineffective. To tackle this problem, we propose a Region Semantically Aligned Network (RSAN), which maps local features of unseen classes to their semantic attributes. Instead of using global features which are obtained by an average pooling layer after an image encoder, we directly utilize the output of the image encoder which maintains local information of the image. Concretely, we obtain each attribute from a specific region of the output and exploit these attributes for recognition. As a result, the knowledge of seen classes can be successfully transferred to unseen classes in a region-bases manner. In addition, we regularize the image encoder through attribute regression with a semantic knowledge to extract robust and attribute-related visual features. Experiments on several standard ZSL datasets reveal the benefit of the proposed RSAN method, outperforming state-of-the-art methods.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 39 of its 68 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

39 of 68 references.

References · 39

  1. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2019Contrastive Multiview Codingcited by 1,297
  4. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  5. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  6. 2013Zero-Shot Learning Through Cross-Modal Transfercited by 237
  7. 2014Evaluation of Output Embeddings for Fine-Grained Image Classificationcited by 166
  8. 2015Label-Embedding for Image Classificationcited by 164
  9. 2020Attribute Prototype Network for Zero-Shot Learningcited by 160
  10. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  11. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  12. 2016An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wildcited by 146
27 more
  1. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  2. 2016Synthesized Classifiers for Zero-Shot Learningcited by 113
  3. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
  4. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  5. 2021Contrastive Embedding for Generalized Zero-Shot Learningcited by 94
  6. 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
  7. 2015Zero-Shot Learning via Semantic Similarity Embeddingcited by 74
  8. 2020Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learningcited by 71
  9. 2021Goal-Oriented Gaze Estimation for Zero-Shot Learningcited by 68
  10. 2020Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learningcited by 64
  11. 2019Semantic-Guided Multi-Attention Localization for Zero-Shot Learningcited by 58
  12. 2020Generalized Zero-Shot Learning Via Over-Complete Distributioncited by 56
  13. 2015Transductive Multi-view Zero-Shot Learningcited by 53
  14. 2015Ridge Regression, Hubness, and Zero-Shot Learningcited by 44
  15. 2020A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learningcited by 34
  16. 2016Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learningcited by 30
  17. 2019From Zero-Shot Learning to Cold-Start Recommendationcited by 29
  18. 2019Locality and compositionality in zero-shot learningcited by 29
  19. 2020Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patchescited by 27
  20. 2019CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Languagecited by 23
  21. 2019Semantically Aligned Bias Reducing Zero Shot Learningcited by 20
  22. 2018Towards Effective Deep Embedding for Zero-Shot Learningcited by 19
  23. 2017Semantically Consistent Regularization for Zero-Shot Recognitioncited by 14
  24. 2019Alleviating Feature Confusion for Generative Zero-shot Learningcited by 10
  25. 2019Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibrationcited by 7
  26. 2017Zero-shot Learning via Shared-Reconstruction-Graph Pursuitcited by 4
  27. 2021Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learningcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.

6 papers.

  1. 2021TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learningcited by 28
  2. 2024Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospectscited by 12
  3. 2024On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Surveycited by 5
  4. 2022Interpretable Few-shot Learning with Online Attribute Selectioncited by 0
  5. 2023Attribute Localization and Revision Network for Zero-Shot Learningcited by 0
  6. 2026Mutually Causal Semantic Distillation Network for Zero-Shot Learningcited by 0