# Simultaneous Edge Alignment and Learning

arXiv: https://arxiv.org/abs/1808.01992 · HTML: https://mlsanity.com/paper/1808.01992/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2018.

## Built on by (most cited first, 22 of 22)

- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [A Unified Efficient Pyramid Transformer for Semantic Segmentation](https://mlsanity.com/paper/2107.14209/) (2021)
- [Delving into Crispness: Guided Label Refinement for Crisp Edge Detection](https://mlsanity.com/paper/2306.15172/) (2023)
- [Contour-Aware Equipotential Learning for Semantic Segmentation](https://mlsanity.com/paper/2210.00223/) (2022)
- [Robust Point Cloud Segmentation with Noisy Annotations](https://mlsanity.com/paper/2212.03242/) (2022)
- [PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions](https://mlsanity.com/paper/2307.14070/) (2023)
- [End-to-End Instance Edge Detection](https://mlsanity.com/paper/2204.02898/) (2022)
- [Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data](https://mlsanity.com/paper/2208.09315/) (2022)
- [HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering](https://mlsanity.com/paper/2304.14446/) (2023)
- [Zero Pixel Directional Boundary by Vector Transform](https://mlsanity.com/paper/2203.08795/) (2022)
- [Learning to Detect Semantic Boundaries with Image-level Class Labels](https://mlsanity.com/paper/2212.07579/) (2022)
- [Boosting Semantic Segmentation with Semantic Boundaries](https://mlsanity.com/paper/2304.09427/) (2023)
- [Uncertainty-aware self-training with expectation maximization basis transformation](https://mlsanity.com/paper/2405.01175/) (2024)
- [Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate](https://mlsanity.com/paper/2112.02781/) (2021)
- [Dunhuang murals contour generation network based on convolution and self-attention fusion](https://mlsanity.com/paper/2212.00935/) (2022)
- [Robust Unsupervised Domain Adaptation by Retaining Confident Entropy via Edge Concatenation](https://mlsanity.com/paper/2310.07149/) (2023)
- [Attention-based fusion of semantic boundary and non-boundary information to improve semantic segmentation](https://mlsanity.com/paper/2108.02840/) (2021)
- [PENet: A Joint Panoptic Edge Detection Network](https://mlsanity.com/paper/2303.08848/) (2023)
- [ECT: Fine-grained Edge Detection with Learned Cause Tokens](https://mlsanity.com/paper/2308.03092/) (2023)
- [Simultaneous Automatic Picking and Manual Picking Refinement for First-Break](https://mlsanity.com/paper/2502.01474/) (2025)
- [Thalia: A Global, Multi-Modal Dataset for Volcanic Activity Monitoring](https://mlsanity.com/paper/2505.17782/) (2025)
- [PEdger++: Practical Edge Detection via Assembling Cross Information](https://mlsanity.com/paper/2508.11961/) (2025)

## Abstract (arXiv)

Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to considerable improvements in this area. Many state of the art edge detection models are learned with fully convolutional networks (FCNs). However, FCN-based edge learning tends to be vulnerable to misaligned labels due to the delicate structure of edges. While such problem was considered in evaluation benchmarks, similar issue has not been explicitly addressed in general edge learning. In this paper, we show that label misalignment can cause considerably degraded edge learning quality, and address this issue by proposing a simultaneous edge alignment and learning framework. To this end, we formulate a probabilistic model where edge alignment is treated as latent variable optimization, and is learned end-to-end during network training. Experiments show several applications of this work, including improved edge detection with state of the art performance, and automatic refinement of noisy annotations.