mlsanityData to 14 Oct 2026

GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder

arXiv 2505.11882 ↗First posted 17 May 2025cs.CV cs.LGcited by 5

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Abstract (arXiv)

Remarkable progress in zero-shot learning (ZSL) has been achieved using generative models. However, existing generative ZSL methods merely generate (imagine) the visual features from scratch guided by the strong class semantic vectors annotated by experts, resulting in suboptimal generative performance and limited scene generalization. To address these and advance ZSL, we propose an inductive variational autoencoder for generative zero-shot learning, dubbed GenZSL. Mimicking human-level concept learning, GenZSL operates by inducting new class samples from similar seen classes using weak class semantic vectors derived from target class names (i.e., CLIP text embedding). To ensure the generation of informative samples for training an effective ZSL classifier, our GenZSL incorporates two key strategies. Firstly, it employs class diversity promotion to enhance the diversity of class semantic vectors. Secondly, it utilizes target class-guided information boosting criteria to optimize the model. Extensive experiments conducted on three popular benchmark datasets showcase the superiority and potential of our GenZSL with significant efficacy and efficiency over f-VAEGAN, e.g., 24.7% performance gains and more than 60× faster training speed on AWA2. Codes are available at https://github.com/shiming-chen/GenZSL.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 40 of its 66 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

40 of 66 references.

References · 40

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  5. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  6. 2016Density estimation using Real NVPcited by 2,042
  7. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 538
  8. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 327
  9. 2022Is synthetic data from generative models ready for image recognition?cited by 320
  10. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 306
  11. 2023Self-regulating Prompts: Foundational Model Adaptation without Forgettingcited by 272
  12. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
28 more
  1. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  2. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
  3. 2020Attribute Prototype Network for Zero-Shot Learningcited by 160
  4. 2016Latent Embeddings for Zero-shot Classificationcited by 129
  5. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  6. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  7. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  8. 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
  9. 2021Goal-Oriented Gaze Estimation for Zero-Shot Learningcited by 68
  10. 2021FREE: Feature Refinement for Generalized Zero-Shot Learningcited by 67
  11. 2022Expanding Small-Scale Datasets with Guided Imaginationcited by 62
  12. 2019Semantic-Guided Multi-Attention Localization for Zero-Shot Learningcited by 58
  13. 2021TransZero: Attribute-guided Transformer for Zero-Shot Learningcited by 54
  14. 2022I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classificationcited by 54
  15. 2023Improving Zero-Shot Generalization for CLIP with Synthesized Promptscited by 51
  16. 2020Invertible Zero-Shot Recognition Flowscited by 48
  17. 2022MSDN: Mutually Semantic Distillation Network for Zero-Shot Learningcited by 48
  18. 2023Dataset Quantizationcited by 48
  19. 2019Transductive Zero-Shot Learning with Visual Structure Constraintcited by 36
  20. 2023ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptationcited by 35
  21. 2022DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learningcited by 33
  22. 2020Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patchescited by 27
  23. 2023Zero-Shot Learning by Harnessing Adversarial Samplescited by 24
  24. 2021Fine-Grained Zero-Shot Learning with DNA as Side Informationcited by 20
  25. 2023Evolving Semantic Prototype Improves Generative Zero-Shot Learningcited by 20
  26. 2024Progressive Semantic-Guided Vision Transformer for Zero-Shot Learningcited by 17
  27. 2024Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learningcited by 16
  28. 2023Image-free Classifier Injection for Zero-Shot Classificationcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Interpretable Zero-Shot Learning with Locally-Aligned Vision-Language Modelcited by 6
  2. 2026IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrievalcited by 1
  3. 2026Learning Adaptive and Visually Aligned Conditions for Generative Zero-Shot Learningcited by 0
  4. 2026Incentivizing Generative Zero-Shot Learning via Outcome-Reward Reinforcement Learning with Visual Cuescited by 0
  5. 2026Visual Anchoring in Diffusion: Multimodal Zero-Shot Skeleton Action Recognitioncited by 0