mlsanityData to 14 Oct 2026

A Survey on Ordinal Regression: Applications, Advances and Prospects

arXiv 2503.00952 ↗First posted 2 Mar 2025cs.CVcited by 5

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Abstract (arXiv)

Ordinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas like facial age estimation, image aesthetics assessment, and even cancer staging, due to its capability to utilize ordered information effectively. More importantly, it also enhances model interpretation by considering category order, aiding the understanding of data trends and causal relationships. Despite significant recent progress, challenges remain, and further investigation of ordinal regression techniques and applications is essential to guide future research. In this survey, we present a comprehensive examination of advances and applications of ordinal regression. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories based on different strategies and objectives: Continuous Space Discretization, Distribution Ordering Learning, and Ambiguous Instance Delving. This categorization enables a structured exploration of diverse insights in ordinal regression problems, providing a framework for a more comprehensive understanding and evaluation of this field and its related applications. To our best knowledge, this is the first systematic survey of ordinal regression, which lays a foundation for future research in this fundamental and generic domain.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 2 Mar 2025.

Builds on

23 of 47 references.

References · 23

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  4. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 991
  5. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  6. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  7. 2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 431
  8. 2016Photo Aesthetics Ranking Network with Attributes and Content Adaptationcited by 125
  9. 2022LocalBins: Improving Depth Estimation by Learning Local Distributionscited by 94
  10. 2021Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regressioncited by 58
  11. 2023IEBins: Iterative Elastic Bins for Monocular Depth Estimationcited by 54
  12. 2022Moving Window Regression: A Novel Approach to Ordinal Regressioncited by 44
11 more
  1. 2022OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regressioncited by 44
  2. 2017Label Distribution Learning Forestscited by 25
  3. 2022Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regressioncited by 23
  4. 2023Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classificationcited by 21
  5. 2024Teach CLIP to Develop a Number Sense for Ordinal Regressioncited by 20
  6. 2018Fine-Grained Age Estimation in the wild with Attention LSTM Networkscited by 18
  7. 2023Ord2Seq: Regarding Ordinal Regression as Label Sequence Predictioncited by 18
  8. 2024CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Promptingcited by 18
  9. 2017Deep Convolutional Neural Network for Age Estimation based on VGG-Face Modelcited by 11
  10. 2024Scalable Autoregressive Monocular Depth Estimationcited by 9
  11. 2023Robust Image Ordinal Regression with Controllable Image Generationcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Learning Ordinal Probabilistic Reward from Preferencescited by 1
  2. 2026A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysiscited by 1
  3. 2026Ordinal Diffusion Models for Color Fundus Imagescited by 0
  4. 2026D3O: Dynamic Distribution Distillation for Ordinal Regressioncited by 0
  5. 2026MAOL: Morphology-Aware Ordinal Learning for Fine-Grained Industrial Defect Severity Gradingcited by 0