# DifNet: Semantic Segmentation by Diffusion Networks

arXiv: https://arxiv.org/abs/1805.08015 · HTML: https://mlsanity.com/paper/1805.08015/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 11 of 11)

- [A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence](https://mlsanity.com/paper/2305.15347/) (2023)
- [FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators](https://mlsanity.com/paper/2310.03420/) (2023)
- [Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace](https://mlsanity.com/paper/2102.09896/) (2021)
- [DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection](https://mlsanity.com/paper/2206.11892/) (2022)
- [Distillation of Diffusion Features for Semantic Correspondence](https://mlsanity.com/paper/2412.03512/) (2024)
- [Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition](https://mlsanity.com/paper/2505.19694/) (2025)
- [MaskDiffusion: Exploiting Pre-trained Diffusion Models for Semantic Segmentation](https://mlsanity.com/paper/2403.11194/) (2024)
- [Diffusion Mechanism in Residual Neural Network: Theory and Applications](https://mlsanity.com/paper/2105.03155/) (2021)
- [RARE: Refine Any Registration of Pairwise Point Clouds via Zero-Shot Learning](https://mlsanity.com/paper/2507.19950/) (2025)
- [FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation](https://mlsanity.com/paper/2408.02075/) (2024)
- [Use square root affinity to regress labels in semantic segmentation](https://mlsanity.com/paper/2103.04990/) (2021)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. OurDifNet is trained end-to-end without complex loss functions.