mlsanityData to 14 Oct 2026

TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning

arXiv 2210.05392 ↗First posted 11 Oct 2022cs.CVcited by 16

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Abstract (arXiv)

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio. To better transfer the knowledge, the proposed Mixup-3T network contains three branches with shared parameters for classifying classes in the source domain, target domain, and intermediate domain. To generate the optimal intermediate domain, the DRGN learns to generate an optimal mix ratio according to the performance on auxiliary target data. Then, the whole TGDM framework is trained via bi-level meta-learning so that TGDM can rectify itself to achieve optimal performance on target data. Extensive experimental results on several benchmark datasets verify the effectiveness of our method.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

33 of 50 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  5. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  6. 2016Matching Networks for One Shot Learningcited by 2,255
  7. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  8. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 901
  9. 2018Learning to Reweight Examples for Robust Deep Learningcited by 675
  10. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  11. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  12. 2020Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixupcited by 261
21 more
  1. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 185
  2. 2018MixUp as Locally Linear Out-Of-Manifold Regularizationcited by 158
  3. 2019Few-Shot Video Classification via Temporal Alignmentcited by 125
  4. 2020Adversarial Feature Hallucination Networks for Few-Shot Learningcited by 110
  5. 2020Self-training for Few-shot Transfer Across Extreme Task Differencescited by 76
  6. 2021Cross-Domain Few-Shot Classification via Adversarial Task Augmentationcited by 63
  7. 2020Explanation-Guided Training for Cross-Domain Few-Shot Classificationcited by 58
  8. 2021Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencodercited by 51
  9. 2020Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognitioncited by 50
  10. 2021Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Datacited by 48
  11. 2021Dual-Awareness Attention for Few-Shot Object Detectioncited by 29
  12. 2022Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learningcited by 23
  13. 2019MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learningcited by 21
  14. 2019MixUp as Directional Adversarial Trainingcited by 14
  15. 2021On the Importance of Distractors for Few-Shot Classificationcited by 14
  16. 2020Learning to Select Base Classes for Few-shot Classificationcited by 11
  17. 2021Cross-domain few-shot learning with unlabelled datacited by 10
  18. 2021Adversarially Optimized Mixup for Robust Classificationcited by 7
  19. 2021Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learningcited by 6
  20. 2021Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decompositioncited by 4
  21. 2021Modular Adaptation for Cross-Domain Few-Shot Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 2; 2025: 7; 2026: 1.

16 papers.

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  1. 2023StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learningcited by 53
  2. 2024Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detectorcited by 43
  3. 2025NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Resultscited by 25
  4. 2025Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detectioncited by 24
  5. 2025EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answeringcited by 21
  6. 2025EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmarkcited by 19
  7. 2025Reliable Few-shot Learning under Dual Noisescited by 17
  8. 2025Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detectioncited by 15
  9. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  10. 2023On the Importance of Spatial Relations for Few-shot Action Recognitioncited by 11
  11. 2023BDC-Adapter: Brownian Distance Covariance for Better Vision-Language Reasoningcited by 6
  12. 2024TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentationcited by 4
4 more
  1. 2023A Survey of Deep Visual Cross-Domain Few-Shot Learningcited by 3
  2. 2023Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rayscited by 1
  3. 2025FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learningcited by 0
  4. 2026SRasP: Self-Reorientation Adversarial Style Perturbation for Cross-Domain Few-Shot Learningcited by 0