# CASENet: Deep Category-Aware Semantic Edge Detection

arXiv: https://arxiv.org/abs/1705.09759 · HTML: https://mlsanity.com/paper/1705.09759/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

## Built on by (most cited first, 30 of 37)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Enhanced Boundary Learning for Glass-like Object Segmentation](https://mlsanity.com/paper/2103.15734/) (2021)
- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Learning Multiple Dense Prediction Tasks from Partially Annotated Data](https://mlsanity.com/paper/2111.14893/) (2021)
- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [Global Spectral Filter Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2210.05567/) (2022)
- [BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation](https://mlsanity.com/paper/2306.08075/) (2023)
- [A Unified Efficient Pyramid Transformer for Semantic Segmentation](https://mlsanity.com/paper/2107.14209/) (2021)
- [Internal-External Boundary Attention Fusion for Glass Surface Segmentation](https://mlsanity.com/paper/2307.00212/) (2023)
- [Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2203.16945/) (2022)
- [Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots](https://mlsanity.com/paper/2311.12651/) (2023)
- [Localization and Mapping using Instance-specific Mesh Models](https://mlsanity.com/paper/2103.04493/) (2021)
- [BoundarySqueeze: Image Segmentation as Boundary Squeezing](https://mlsanity.com/paper/2105.11668/) (2021)
- [TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization](https://mlsanity.com/paper/2108.04508/) (2021)
- [Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation](https://mlsanity.com/paper/2205.15720/) (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)
- [Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2212.12402/) (2022)
- [LiftFormer: Lifting and Frame Theory Based Monocular Depth Estimation Using Depth and Edge Oriented Subspace Representation](https://mlsanity.com/paper/2604.06576/) (2026)
- [Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images](https://mlsanity.com/paper/2109.08915/) (2021)
- [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)
- [TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency](https://mlsanity.com/paper/2210.05152/) (2022)
- [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)
- [Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation](https://mlsanity.com/paper/2312.04744/) (2023)

## Abstract (arXiv)

Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard data sets such as SBD and Cityscapes.