# Dynamic Feature Fusion for Semantic Edge Detection

arXiv: https://arxiv.org/abs/1902.09104 · HTML: https://mlsanity.com/paper/1902.09104/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 16 of 16)

- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2108.08063/) (2021)
- [Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots](https://mlsanity.com/paper/2311.12651/) (2023)
- [PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions](https://mlsanity.com/paper/2307.14070/) (2023)
- [Segmenting Object Affordances: Reproducibility and Sensitivity to Scale](https://mlsanity.com/paper/2409.01814/) (2024)
- [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)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [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)
- [Holistic Geometric Feature Learning for Structured Reconstruction](https://mlsanity.com/paper/2309.09622/) (2023)
- [BoundMatch: Boundary detection applied to semi-supervised segmentation](https://mlsanity.com/paper/2503.23519/) (2025)
- [PEdger++: Practical Edge Detection via Assembling Cross Information](https://mlsanity.com/paper/2508.11961/) (2025)

## Abstract (arXiv)

Features from multiple scales can greatly benefit the semantic edge detection task if they are well fused. However, the prevalent semantic edge detection methods apply a fixed weight fusion strategy where images with different semantics are forced to share the same weights, resulting in universal fusion weights for all images and locations regardless of their different semantics or local context. In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively. This is achieved by a proposed weight learner to infer proper fusion weights over multi-level features for each location of the feature map, conditioned on the specific input. In this way, the heterogeneity in contributions made by different locations of feature maps and input images can be better considered and thus help produce more accurate and sharper edge predictions. We show that our model with the novel dynamic feature fusion is superior to fixed weight fusion and also the na"ive location-invariant weight fusion methods, via comprehensive experiments on benchmarks Cityscapes and SBD. In particular, our method outperforms all existing well established methods and achieves new state-of-the-art.