mlsanityData to 7 Oct 2026

Few-shot Partial Multi-view Learning

arXiv 2105.02046 ↗First posted 5 May 2021cs.CV cs.LGcited by 3

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Abstract (arXiv)

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 80 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

36 of 80 references.

References · 36

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24 more
  1. 2014Distributed Representations of Sentences and Documentscited by 677
  2. 2014Neural Turing Machinescited by 630
  3. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  4. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  5. 2020Few-Shot Class-Incremental Learningcited by 249
  6. 2018Low-Shot Learning from Imaginary Datacited by 181
  7. 2020Adversarial Feature Hallucination Networks for Few-Shot Learningcited by 110
  8. 2019Prototype Rectification for Few-Shot Learningcited by 96
  9. 2020Boosting Few-Shot Learning With Adaptive Margin Losscited by 83
  10. 2020Leveraging the Feature Distribution in Transfer-based Few-Shot Learningcited by 72
  11. 2020Deep Partial Multi-View Learningcited by 71
  12. 2018Memory Matching Networks for One-Shot Image Recognitioncited by 65
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  14. 2021Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learningcited by 56
  15. 2020Prototype Completion with Primitive Knowledge for Few-Shot Learningcited by 50
  16. 2019Few-Shot Learning via Saliency-guided Hallucination of Samplescited by 49
  17. 2020Multimodal Prototypical Networks for Few-shot Learningcited by 48
  18. 2019Few-Shot Learning with Global Class Representationscited by 44
  19. 2019Doubly Aligned Incomplete Multi-view Clusteringcited by 40
  20. 2017Low-shot learning with large-scale diffusioncited by 33
  21. 2019Adaptive Posterior Learning: few-shot learning with a surprise-based memory modulecited by 25
  22. 2020Cooperative Bi-path Metric for Few-shot Learningcited by 15
  23. 2020Memory-Augmented Relation Network for Few-Shot Learningcited by 10
  24. 2020Improving Few-Shot Learning using Composite Rotation based Auxiliary Taskcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2026Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigationcited by 7
  2. 2024CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interactioncited by 5
  3. 2026GuardAlign: Test-time Safety Alignment in Multimodal Large Language Modelscited by 1