# RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses

arXiv: https://arxiv.org/abs/2403.01795 · HTML: https://mlsanity.com/paper/2403.01795/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 54 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2003.03773/) (2020)
- [On the uncertainty of self-supervised monocular depth estimation](https://mlsanity.com/paper/2005.06209/) (2020)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Learning to predict crisp boundaries](https://mlsanity.com/paper/1807.10097/) (2018)
- [Bi-Directional Cascade Network for Perceptual Edge Detection](https://mlsanity.com/paper/1902.10903/) (2019)
- [BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors](https://mlsanity.com/paper/1903.03838/) (2019)
- [Fast Edge Detection Using Structured Forests](https://mlsanity.com/paper/1406.5549/) (2014)
- [Optimizing Rank-based Metrics with Blackbox Differentiation](https://mlsanity.com/paper/1912.03500/) (2019)
- [DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection](https://mlsanity.com/paper/1412.1123/) (2014)
- [Efficient Uncertainty Estimation for Semantic Segmentation in Videos](https://mlsanity.com/paper/1807.11037/) (2018)
- [Towards Accurate One-Stage Object Detection with AP-Loss](https://mlsanity.com/paper/1904.06373/) (2019)
- [Uncertainty Estimation in One-Stage Object Detection](https://mlsanity.com/paper/1905.10296/) (2019)
- [Pushing the Boundaries of Boundary Detection using Deep Learning](https://mlsanity.com/paper/1511.07386/) (2015)
- [Object Contour Detection with a Fully Convolutional Encoder-Decoder Network](https://mlsanity.com/paper/1603.04530/) (2016)
- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction](https://mlsanity.com/paper/1801.00524/) (2018)
- [Gradient-based Uncertainty for Monocular Depth Estimation](https://mlsanity.com/paper/2208.02005/) (2022)
- [Oriented Edge Forests for Boundary Detection](https://mlsanity.com/paper/1412.4181/) (2014)
- [A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection](https://mlsanity.com/paper/2009.13592/) (2020)
- [The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector](https://mlsanity.com/paper/2303.11828/) (2023)
- [High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision](https://mlsanity.com/paper/1504.06201/) (2015)
- [DR Loss: Improving Object Detection by Distributional Ranking](https://mlsanity.com/paper/1907.10156/) (2019)
- [AP-Loss for Accurate One-Stage Object Detection](https://mlsanity.com/paper/2008.07294/) (2020)

## Built on by (most cited first, 19 of 19)

- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity Edge Detection](https://mlsanity.com/paper/2412.12892/) (2024)
- [Generative Edge Detection with Stable Diffusion](https://mlsanity.com/paper/2410.03080/) (2024)
- [Boosting Edge Detection with Pixel-wise Feature Selection: The Extractor-Selector Paradigm](https://mlsanity.com/paper/2501.02534/) (2025)
- [A Comprehensive Study for Edge Detection: Ideal-Prior Guidance Learning, High-Precision Architecture, and Better Distinguishability Evaluation Benchmark](https://mlsanity.com/paper/2407.19992/) (2024)
- [EDMB: Edge Detector with Mamba](https://mlsanity.com/paper/2501.04846/) (2025)
- [Symmetrization Weighted Binary Cross-Entropy: Modeling Perceptual Asymmetry for Human-Consistent Neural Edge Detection](https://mlsanity.com/paper/2501.13365/) (2025)
- [Learning to utilize image second-order derivative information for crisp edge detection](https://mlsanity.com/paper/2406.05779/) (2024)
- [EDCSSM: Edge Detection with Convolutional State Space Model](https://mlsanity.com/paper/2409.01609/) (2024)
- [Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking](https://mlsanity.com/paper/2502.17766/) (2025)
- [Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection](https://mlsanity.com/paper/2504.15770/) (2025)
- [Binarization-Aware Adjuster for Discrete Decision Learning with an Application to Edge Detection](https://mlsanity.com/paper/2506.12460/) (2025)
- [MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision](https://mlsanity.com/paper/2602.20689/) (2026)
- [FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)](https://mlsanity.com/paper/2506.05095/) (2025)
- [Edge-Boundary-Texture Loss: A Tri-Class Generalization of Weighted Binary Cross-Entropy for Enhanced Edge Detection](https://mlsanity.com/paper/2507.06569/) (2025)
- [MS2Edge: Towards Energy-Efficient and Crisp Edge Detection with Multi-Scale Residual Learning in SNNs](https://mlsanity.com/paper/2511.13735/) (2025)
- [A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification](https://mlsanity.com/paper/2603.07571/) (2026)
- [R2E-VID: Two-Stage Robust Routing via Temporal Gating for Elastic Edge-Cloud Video Inference](https://mlsanity.com/paper/2604.09681/) (2026)
- [RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark](https://mlsanity.com/paper/2608.30820/) (2026)

## Abstract (arXiv)

Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators. Existing solutions address P1 using class-balanced cross-entropy loss and dice loss and P2 by only predicting edges agreed upon by most annotators. In this paper, we propose RankED, a unified ranking-based approach that addresses both the imbalance problem (P1) and the uncertainty problem (P2). RankED tackles these two problems with two components: One component which ranks positive pixels over negative pixels, and the second which promotes high confidence edge pixels to have more label certainty. We show that RankED outperforms previous studies and sets a new state-of-the-art on NYUD-v2, BSDS500 and Multi-cue datasets. Code is available at https://ranked-cvpr24.github.io.