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DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning

arXiv 2103.10764 ↗First posted 19 Mar 2021cs.CVcited by 0

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Abstract (arXiv)

Generative based strategy has shown great potential in the Generalized Zero-Shot Learning task. However, it suffers severe generalization problem due to lacking of feature diversity for unseen classes to train a good classifier. In this paper, we propose to enhance the generalizability of GZSL models via improving feature diversity of unseen classes. For this purpose, we present a novel Diverse Feature Synthesis (DFS) model. Different from prior works that solely utilize semantic knowledge in the generation process, DFS leverages visual knowledge with semantic one in a unified way, thus deriving class-specific diverse feature samples and leading to robust classifier for recognizing both seen and unseen classes in the testing phase. To simplify the learning, DFS represents visual and semantic knowledge in the aligned space, making it able to produce good feature samples with a low-complexity implementation. Accordingly, DFS is composed of two consecutive generators: an aligned feature generator, transferring semantic and visual representations into aligned features; a synthesized feature generator, producing diverse feature samples of unseen classes in the aligned space. We conduct comprehensive experiments to verify the efficacy of DFS. Results demonstrate its effectiveness to generate diverse features for unseen classes, leading to superior performance on multiple benchmarks. Code will be released upon acceptance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 19 Mar 2021.

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32 of 47 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  5. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  6. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  7. 2013Zero-Shot Learning Through Cross-Modal Transfercited by 237
  8. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  9. 2016Learning Deep Representations of Fine-grained Visual Descriptionscited by 182
  10. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
  11. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  12. 2013Zero-Shot Learning by Convex Combination of Semantic Embeddingscited by 135
20 more
  1. 2017Semantic Autoencoder for Zero-Shot Learningcited by 133
  2. 2020Meta-Transfer Learning for Zero-Shot Super-Resolutioncited by 116
  3. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  4. 2016Synthesized Classifiers for Zero-Shot Learningcited by 113
  5. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
  6. 2018Multi-modal Cycle-consistent Generalized Zero-Shot Learningcited by 87
  7. 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
  8. 2020Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applicationscited by 79
  9. 2015Zero-Shot Learning via Semantic Similarity Embeddingcited by 74
  10. 2017A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoderscited by 64
  11. 2020Motion-Attentive Transition for Zero-Shot Video Object Segmentationcited by 62
  12. 2018Generative Dual Adversarial Network for Generalized Zero-shot Learningcited by 61
  13. 2017Learning Robust Visual-Semantic Embeddingscited by 56
  14. 2020Generalized Zero-Shot Learning Via Over-Complete Distributioncited by 56
  15. 2015Zero-Shot Learning via Joint Latent Similarity Embeddingcited by 39
  16. 2017A Simple Exponential Family Framework for Zero-Shot Learningcited by 38
  17. 2018Preserving Semantic Relations for Zero-Shot Learningcited by 35
  18. 2019Semantically Aligned Bias Reducing Zero Shot Learningcited by 20
  19. 2019Context-Aware Zero-Shot Recognitioncited by 12
  20. 2019Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognitioncited by 5

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