mlsanityData to 7 Oct 2026

Dual Expert Distillation Network for Generalized Zero-Shot Learning

arXiv 2404.16348 ↗First posted 25 Apr 2024cs.CVcited by 4

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Abstract (arXiv)

Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes, ignoring two crucial issues: 1) the inherent asymmetry of attributes; and 2) the unutilized channel information. This paper addresses these issues by introducing a simple yet effective approach, dubbed Dual Expert Distillation Network (DEDN), where two experts are dedicated to coarse- and fine-grained visual-attribute modeling, respectively. Concretely, one coarse expert, namely cExp, has a complete perceptual scope to coordinate visual-attribute similarity metrics across dimensions, and moreover, another fine expert, namely fExp, consists of multiple specialized subnetworks, each corresponds to an exclusive set of attributes. Two experts cooperatively distill from each other to reach a mutual agreement during training. Meanwhile, we further equip DEDN with a newly designed backbone network, i.e., Dual Attention Network (DAN), which incorporates both region and channel attention information to fully exploit and leverage visual semantic knowledge. Experiments on various benchmark datasets indicate a new state-of-the-art.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 57 of its 83 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

57 of 83 references.

References · 57

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
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38 more
  1. 2018Multi-modal Cycle-consistent Generalized Zero-Shot Learningcited by 87
  2. 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
  3. 2021FREE: Feature Refinement for Generalized Zero-Shot Learningcited by 67
  4. 2019Episode-based Prototype Generating Network for Zero-Shot Learningcited by 66
  5. 2019Transferable Contrastive Network for Generalized Zero-Shot Learningcited by 64
  6. 2020Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learningcited by 64
  7. 2019Semantic-Guided Multi-Attention Localization for Zero-Shot Learningcited by 58
  8. 2021HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learningcited by 57
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  10. 2021Semantics Disentangling for Generalized Zero-Shot Learningcited by 56
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  15. 2020A Novel Perspective to Zero-shot Learning: Towards an Alignment of Manifold Structures via Semantic Feature Expansioncited by 31
  16. 2020Attribute Propagation Network for Graph Zero-shot Learningcited by 29
  17. 2023SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detectioncited by 28
  18. 2021Dual Progressive Prototype Network for Generalized Zero-Shot Learningcited by 26
  19. 2021Personalized Federated Learning with Contextualized Generalizationcited by 23
  20. 2022CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streamingcited by 23
  21. 2022Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attentioncited by 21
  22. 2019AMS-SFE: Towards an Alignment of Manifold Structures via Semantic Feature Expansion for Zero-shot Learningcited by 19
  23. 2024Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognitioncited by 19
  24. 2019Adaptive Adjustment with Semantic Feature Space for Zero-Shot Recognitioncited by 18
  25. 2019Position-Aware Convolutional Networks for Traffic Predictioncited by 18
  26. 2022Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruningcited by 17
  27. 2023Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learningcited by 17
  28. 2023DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learningcited by 17
  29. 2021Learning Robust Visual-semantic Mapping for Zero-shot Learningcited by 16
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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognitioncited by 13
  2. 2024On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Surveycited by 5
  3. 2025FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Datacited by 0
  4. 2026SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classificationcited by 0