mlsanityData to 14 Oct 2026

RankDNN: Learning to Rank for Few-shot Learning

arXiv 2211.15320 ↗First posted 28 Nov 2022cs.CVcited by 7

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Abstract (arXiv)

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankMLP can be built on top of any state-of-the-art feature extractors, and our entire deep neural network is called the ranking deep neural network, or RankDNN. Meanwhile, RankDNN can be flexibly fused with other post-processing methods. During the meta test, RankDNN ranks support images according to their similarity with the query samples, and each query sample is assigned the class label of its nearest neighbor. Experiments demonstrate that RankDNN can effectively improve the performance of its baselines based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning benchmarks, including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments on the cross-domain challenge demonstrate the superior transferability of RankDNN.The code is available at: https://github.com/guoqianyu-alberta/RankDNN.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 52 of its 80 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

52 of 80 references.

References · 52

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38 more
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50 most cited of 52 All 52 (JSON)

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 3.

7 papers.

  1. 2023Context-Aware Meta-Learningcited by 21
  2. 2024KNN Transformer with Pyramid Prompts for Few-Shot Learningcited by 3
  3. 2023Plug-and-Play Feature Generation for Few-Shot Medical Image Classificationcited by 0
  4. 2024Machine Learning and Data Analysis Using Posets: A Surveycited by 0
  5. 2025Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learningcited by 0
  6. 2025Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Needcited by 0
  7. 2025Agentic Meta-Orchestrator for Multi-task Copilotscited by 0