mlsanityData to 7 Oct 2026

A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios

arXiv 2302.04060 ↗First posted 8 Feb 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Embedding-aware generative model (EAGM) addresses the data insufficiency problem for zero-shot learning (ZSL) by constructing a generator between semantic and visual feature spaces. Thanks to the predefined benchmark and protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue that it is time to take a step back and reconsider the embedding-aware generative paradigm. The main work of this paper is two-fold. First, the embedding features in benchmark datasets are somehow overlooked, which potentially limits the performance of EAGMs, while most researchers focus on how to improve EAGMs. Therefore, we conduct a systematic evaluation of ten representative EAGMs and prove that even embarrassedly simple modifications on the embedding features can improve the performance of EAGMs for ZSL remarkably. So it's time to pay more attention to the current embedding features in benchmark datasets. Second, based on five benchmark datasets, each with six any-shot learning scenarios, we systematically compare the performance of ten typical EAGMs for the first time, and we give a strong baseline for zero-shot learning (ZSL) and few-shot learning (FSL). Meanwhile, a comprehensive generative model repository, namely, generative any-shot learning (GASL) repository, is provided, which contains the models, features, parameters, and scenarios of EAGMs for ZSL and FSL. Any results in this paper can be readily reproduced with only one command line based on GASL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 68 of its 116 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

68 of 116 references.

References · 68

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  9. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  10. 2014Going Deeper with Convolutionscited by 4,231
  11. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  12. 2016Matching Networks for One Shot Learningcited by 2,246
38 more
  1. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  2. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  3. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,096
  4. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  5. 2017Wasserstein GANcited by 635
  6. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  7. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  8. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  9. 2015LSTM: A Search Space Odysseycited by 315
  10. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  11. 2017Zero-Shot Learning -- The Good, the Bad and the Uglycited by 254
  12. 2015Generative Moment Matching Networkscited by 252
  13. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  14. 2016Learning Deep Representations of Fine-grained Visual Descriptionscited by 182
  15. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
  16. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  17. 2018Zero-shot Recognition via Semantic Embeddings and Knowledge Graphscited by 155
  18. 2016An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wildcited by 146
  19. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  20. 2013Generalized Denoising Auto-Encoders as Generative Modelscited by 113
  21. 2016Synthesized Classifiers for Zero-Shot Learningcited by 113
  22. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
  23. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  24. 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
  25. 2020Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learningcited by 71
  26. 2014PCANet: A Simple Deep Learning Baseline for Image Classification?cited by 70
  27. 2021FREE: Feature Refinement for Generalized Zero-Shot Learningcited by 67
  28. 2019Episode-based Prototype Generating Network for Zero-Shot Learningcited by 66
  29. 2017A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoderscited by 64
  30. 2020Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learningcited by 64
  31. 2018Generative Dual Adversarial Network for Generalized Zero-shot Learningcited by 61
  32. 2020Generalized Zero-Shot Learning Via Over-Complete Distributioncited by 56
  33. 2016Generative Image Modeling using Style and Structure Adversarial Networkscited by 50
  34. 2020Invertible Zero-Shot Recognition Flowscited by 48
  35. 2020Multimodal Prototypical Networks for Few-shot Learningcited by 48
  36. 2018Transductive Unbiased Embedding for Zero-Shot Learningcited by 44
  37. 2021Knowledge-aware Zero-Shot Learning: Survey and Perspectivecited by 41
  38. 2017A Simple Exponential Family Framework for Zero-Shot Learningcited by 38

50 most cited of 68 All 68 (JSON)

Built on by

Not cited yet.