mlsanityData to 7 Oct 2026

Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning

arXiv 2109.01295 ↗First posted 3 Sep 2021cs.CV cs.AI cs.CLcited by 6

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Abstract (arXiv)

Semantic information provides intra-class consistency and inter-class discriminability beyond visual concepts, which has been employed in Few-Shot Learning (FSL) to achieve further gains. However, semantic information is only available for labeled samples but absent for unlabeled samples, in which the embeddings are rectified unilaterally by guiding the few labeled samples with semantics. Therefore, it is inevitable to bring a cross-modal bias between semantic-guided samples and nonsemantic-guided samples, which results in an information asymmetry problem. To address this problem, we propose a Modal-Alternating Propagation Network (MAP-Net) to supplement the absent semantic information of unlabeled samples, which builds information symmetry among all samples in both visual and semantic modalities. Specifically, the MAP-Net transfers the neighbor information by the graph propagation to generate the pseudo-semantics for unlabeled samples guided by the completed visual relationships and rectify the feature embeddings. In addition, due to the large discrepancy between visual and semantic modalities, we design a Relation Guidance (RG) strategy to guide the visual relation vectors via semantics so that the propagated information is more beneficial. Extensive experimental results on three semantic-labeled datasets, i.e., Caltech-UCSD-Birds 200-2011, SUN Attribute Database, and Oxford 102 Flower, have demonstrated that our proposed method achieves promising performance and outperforms the state-of-the-art approaches, which indicates the necessity of information symmetry.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 51 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

37 of 51 references.

References · 37

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25 more
  1. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 293
  2. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 208
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2022Memorizing Complementation Network for Few-Shot Class-Incremental Learningcited by 30
  2. 2022TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learningcited by 16
  3. 2026Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learningcited by 4
  4. 2023Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learningcited by 2
  5. 2023P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classificationcited by 1
  6. 2023Compositional Prototypical Networks for Few-Shot Classificationcited by 1