# Multi-domain semantic segmentation with overlapping labels

arXiv: https://arxiv.org/abs/2108.11224 · HTML: https://mlsanity.com/paper/2108.11224/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](https://mlsanity.com/paper/1811.10200/) (2018)
- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https://mlsanity.com/paper/1812.01593/) (2018)
- [Seamless Scene Segmentation](https://mlsanity.com/paper/1905.01220/) (2019)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [Object Detection with a Unified Label Space from Multiple Datasets](https://mlsanity.com/paper/2008.06614/) (2020)
- [Universal Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1811.10323/) (2018)
- [Dynamic-structured Semantic Propagation Network](https://mlsanity.com/paper/1803.06067/) (2018)
- [Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation](https://mlsanity.com/paper/1803.05675/) (2018)
- [Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images](https://mlsanity.com/paper/1905.05661/) (2019)
- [Multi-domain semantic segmentation with pyramidal fusion](https://mlsanity.com/paper/2009.01636/) (2020)

## Built on by (most cited first, 14 of 14)

- [INTERN: A New Learning Paradigm Towards General Vision](https://mlsanity.com/paper/2111.08687/) (2021)
- [OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts](https://mlsanity.com/paper/2507.05427/) (2025)
- [RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion](https://mlsanity.com/paper/2407.21631/) (2024)
- [The Missing Link: Finding label relations across datasets](https://mlsanity.com/paper/2206.04453/) (2022)
- [COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving](https://mlsanity.com/paper/2311.03017/) (2023)
- [Automatic universal taxonomies for multi-domain semantic segmentation](https://mlsanity.com/paper/2207.08445/) (2022)
- [Weakly supervised training of universal visual concepts for multi-domain semantic segmentation](https://mlsanity.com/paper/2212.10340/) (2022)
- [COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets](https://mlsanity.com/paper/2202.06884/) (2022)
- [Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets](https://mlsanity.com/paper/2506.04737/) (2025)
- [An Empirical Study on Multi-Domain Robust Semantic Segmentation](https://mlsanity.com/paper/2212.04221/) (2022)
- [Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs](https://mlsanity.com/paper/2407.10534/) (2024)
- [Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation](https://mlsanity.com/paper/2409.09893/) (2024)
- [Towards Establishing Systematic Classification Requirements for Automated Driving](https://mlsanity.com/paper/2307.14058/) (2023)
- [Knowledge Distillation for Semantic Segmentation: A Label Space Unification Approach](https://mlsanity.com/paper/2502.19177/) (2025)

## Abstract (arXiv)

Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on many datasets becomes a method of choice towards graceful degradation in unusual scenes. Unfortunately, different datasets often use incompatible labels. For instance, the Cityscapes road class subsumes all driving surfaces, while Vistas defines separate classes for road markings, manholes etc. We address this challenge by proposing a principled method for seamless learning on datasets with overlapping classes based on partial labels and probabilistic loss. Our method achieves competitive within-dataset and cross-dataset generalization, as well as ability to learn visual concepts which are not separately labeled in any of the training datasets. Experiments reveal competitive or state-of-the-art performance on two multi-domain dataset collections and on the WildDash 2 benchmark.