# Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness

arXiv: https://arxiv.org/abs/2503.18445 · HTML: https://mlsanity.com/paper/2503.18445/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 50 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 30 of 35)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Multimodal Token Fusion for Vision Transformers](https://mlsanity.com/paper/2204.08721/) (2022)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation](https://mlsanity.com/paper/2007.09183/) (2020)
- [Delivering Arbitrary-Modal Semantic Segmentation](https://mlsanity.com/paper/2303.01480/) (2023)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis](https://mlsanity.com/paper/2011.06961/) (2020)
- [ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation](https://mlsanity.com/paper/2108.10528/) (2021)
- [DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation](https://mlsanity.com/paper/2309.09668/) (2023)
- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation](https://mlsanity.com/paper/2004.04534/) (2020)
- [FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation](https://mlsanity.com/paper/2110.08988/) (2021)
- [GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer](https://mlsanity.com/paper/2406.01210/) (2024)
- [ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data](https://mlsanity.com/paper/2008.08974/) (2020)
- [ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation](https://mlsanity.com/paper/2108.12382/) (2021)
- [RGB-T Semantic Segmentation with Location, Activation, and Sharpening](https://mlsanity.com/paper/2210.14530/) (2022)
- [StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation](https://mlsanity.com/paper/2408.01343/) (2024)
- [PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model](https://mlsanity.com/paper/2204.02681/) (2022)
- [CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes](https://mlsanity.com/paper/2410.10791/) (2024)
- [MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty](https://mlsanity.com/paper/2401.12761/) (2024)
- [RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer](https://mlsanity.com/paper/2210.07124/) (2022)
- [Learning Modality-agnostic Representation for Semantic Segmentation from Any Modalities](https://mlsanity.com/paper/2407.11351/) (2024)
- [Centering the Value of Every Modality: Towards Efficient and Resilient Modality-agnostic Semantic Segmentation](https://mlsanity.com/paper/2407.11344/) (2024)
- [Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters](https://mlsanity.com/paper/2207.01331/) (2022)
- [Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts](https://mlsanity.com/paper/2412.04220/) (2024)

## Built on by (most cited first, 18 of 18)

- [MLLMs are Deeply Affected by Modality Bias](https://mlsanity.com/paper/2505.18657/) (2025)
- [Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?](https://mlsanity.com/paper/2505.11907/) (2025)
- [How Do Vision-Language Models Process Conflicting Information Across Modalities?](https://mlsanity.com/paper/2507.01790/) (2025)
- [Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization](https://mlsanity.com/paper/2505.06635/) (2025)
- [Split Matching for Inductive Zero-shot Semantic Segmentation](https://mlsanity.com/paper/2505.05023/) (2025)
- [TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understanding](https://mlsanity.com/paper/2511.07007/) (2025)
- [EGFormer: Towards Efficient and Generalizable Multimodal Semantic Segmentation](https://mlsanity.com/paper/2505.14014/) (2025)
- [ModalImmune: Immunity Driven Unlearning via Self Destructive Training](https://mlsanity.com/paper/2602.16197/) (2026)
- [GeomPrompt: Geometric Prompt Learning for RGB-D Semantic Segmentation Under Missing and Degraded Depth](https://mlsanity.com/paper/2604.11585/) (2026)
- [DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities](https://mlsanity.com/paper/2505.08283/) (2025)
- [RMMSS: Towards Advanced Robust Multi-Modal Semantic Segmentation with Hybrid Prototype Distillation and Feature Selection](https://mlsanity.com/paper/2505.12861/) (2025)
- [BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation](https://mlsanity.com/paper/2506.03675/) (2025)
- [Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation](https://mlsanity.com/paper/2506.22032/) (2025)
- [Robust Multimodal Semantic Segmentation with Balanced Modality Contributions](https://mlsanity.com/paper/2509.24505/) (2025)
- [J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization](https://mlsanity.com/paper/2510.09953/) (2025)
- [ModalPatch: A Plug-and-Play Module for Robust Multi-Modal 3D Object Detection under Modality Drop](https://mlsanity.com/paper/2603.02481/) (2026)
- [UMSS: Towards Unsupervised Multi-modal Semantic Segmentation](https://mlsanity.com/paper/2607.12372/) (2026)
- [Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout](https://mlsanity.com/paper/2607.20326/) (2026)

## Abstract (arXiv)

Multi-modal semantic segmentation (MMSS) addresses the limitations of single-modality data by integrating complementary information across modalities. Despite notable progress, a significant gap persists between research and real-world deployment due to variability and uncertainty in multi-modal data quality. Robustness has thus become essential for practical MMSS applications. However, the absence of standardized benchmarks for evaluating robustness hinders further advancement. To address this, we first survey existing MMSS literature and categorize representative methods to provide a structured overview. We then introduce a robustness benchmark that evaluates MMSS models under three scenarios: Entire-Missing Modality (EMM), Random-Missing Modality (RMM), and Noisy Modality (NM). From a probabilistic standpoint, we model modality failure under two conditions: (1) all damaged combinations are equally probable; (2) each modality fails independently following a Bernoulli distribution. Based on these, we propose four metrics-mIoUAvgEMM, mIoUEEMM, mIoUAvgRMM, and mIoUERMM-to assess model robustness under EMM and RMM. This work provides the first dedicated benchmark for MMSS robustness, offering new insights and tools to advance the field. Source code is available at https://github.com/Chenfei-Liao/Multi-Modal-Semantic-Segmentation-Robustness-Benchmark.