Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
On the map: State-space sequence models (S4, Mamba) → Vision Mamba backbones
Abstract (arXiv)
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments. Leveraging additional modalities (X-modality) like thermal and depth alongside traditional RGB provides complementary information, enabling more robust and reliable prediction. In this work, we introduce Sigma, a Siamese Mamba network for multi-modal semantic segmentation utilizing the advanced Mamba. Unlike conventional methods that rely on CNNs, with their limited local receptive fields, or Vision Transformers (ViTs), which offer global receptive fields at the cost of quadratic complexity, our model achieves global receptive fields with linear complexity. By employing a Siamese encoder and innovating a Mamba-based fusion mechanism, we effectively select essential information from different modalities. A decoder is then developed to enhance the channel-wise modeling ability of the model. Our proposed method is rigorously evaluated on both RGB-Thermal and RGB-Depth semantic segmentation tasks, demonstrating its superiority and marking the first successful application of State Space Models (SSMs) in multi-modal perception tasks. Code is available at https://github.com/zifuwan/Sigma.
Key facts. Cited by 71 papers in the map (data through 14 Oct 2026). Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 49 of its 77 references that are in the map. First posted on arXiv 5 Apr 2024.
Builds on
49 of 77 references.
Closest ancestors · 1
Same direction, newest first
- 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model17 Jan
Other references · 48
- 2017Attention Is All You Needcited by 60,361
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
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- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
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- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
- 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
- 2018Unified Perceptual Parsing for Scene Understandingcited by 1,271
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- 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
- 2022Simplified State Space Layers for Sequence Modelingcited by 788
- 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
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- 2022Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detectioncited by 317
- 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 314
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- 2020Deep Multimodal Fusion by Channel Exchangingcited by 118
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- 2018Learning a Discriminative Feature Network for Semantic Segmentationcited by 114
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- 2019ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentationcited by 107
- 2022InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understandingcited by 89
- 2019PST900: RGB-Thermal Calibration, Dataset and Segmentation Networkcited by 85
- 2020Efficient RGB-D Semantic Segmentation for Indoor Scene Analysiscited by 84
- 2021ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentationcited by 77
- 2016Full-Resolution Residual Networks for Semantic Segmentation in Street Scenescited by 71
- 2023DFormer: Rethinking RGBD Representation Learning for Semantic Segmentationcited by 60
- 2020Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentationcited by 48
- 2021FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentationcited by 45
- 2023Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Taskscited by 41
- 2022Efficient Multi-Task RGB-D Scene Analysis for Indoor Environmentscited by 33
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- 2023PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentationcited by 26
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- 2024HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generationcited by 14
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- 20242023 Low-Power Computer Vision Challenge (LPCVC) Summarycited by 2
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Citing papers per year: 2024: 22; 2025: 30; 2026: 19.
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- 2025Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detectioncited by 6
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- 2025Beyond Appearance: Geometric Cues for Robust Video Instance Segmentationcited by 3
- 2026NeXt2Former-CD: Efficient Remote Sensing Change Detection with Modern Vision Architecturescited by 3
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