# COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal Object Detection arXiv: https://arxiv.org/abs/2412.18076 ยท HTML: https://mlsanity.com/paper/2412.18076/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba). - Builds on 11 of its 50 references that are in the map. - First posted on arXiv 24 Dec 2024. ## Placed in - State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/) ## Builds on (most cited first, 11 of 11) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020) - [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024) - [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024) - [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024) - [LocalMamba: Visual State Space Model with Windowed Selective Scan](https://mlsanity.com/paper/2403.09338/) (2024) - [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018) - [Cross-Modality Fusion Transformer for Multispectral Object Detection](https://mlsanity.com/paper/2111.00273/) (2021) - [FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba](https://mlsanity.com/paper/2404.09498/) (2024) ## Built on by (most cited first, 18 of 18) - [M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection](https://mlsanity.com/paper/2505.10931/) (2025) - [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025) - [LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba](https://mlsanity.com/paper/2508.11849/) (2025) - [Rethinking Efficient Mixture-of-Experts for Remote Sensing Modality-Missing Classification](https://mlsanity.com/paper/2511.11460/) (2025) - [AlignFreeNet: Is Cross-Modal Pre-Alignment Necessary? An End-to-End Alignment-Free Lightweight Network for Visible-Infrared Object Detection](https://mlsanity.com/paper/2507.20146/) (2025) - [Representation Space Constrained Learning with Modality Decoupling for Multimodal Object Detection](https://mlsanity.com/paper/2511.15433/) (2025) - [Multi-modal Collaborative Optimization and Expansion Network for Event-assisted Single-eye Expression Recognition](https://mlsanity.com/paper/2505.12007/) (2025) - [LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection](https://mlsanity.com/paper/2506.21018/) (2025) - [M2I2HA: Multi-modal Object Detection Based on Intra- and Inter-Modal Hypergraph Attention](https://mlsanity.com/paper/2601.14776/) (2026) - [LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection](https://mlsanity.com/paper/2605.20667/) (2026) - [Cross-Modality Feature Fusion Based on Structured State Space Duality for Multimodal Image Registration Network](https://mlsanity.com/paper/2606.03341/) (2026) - [Progressive Pixel-Neighborhood Deformable Cross-Attention for Multispectral Object Detection](https://mlsanity.com/paper/2606.24092/) (2026) - [CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection](https://mlsanity.com/paper/2608.06205/) (2026) - [Bridging Severe Cross-Modal Misalignment: End-to-End Visible-Infrared Object Detection via Explicit Feature-Domain Affine Registration](https://mlsanity.com/paper/2608.10680/) (2026) - [InterPruner: Interactive Structured Pruning via Taylor-Implicit Criterion and Language-Prior Modulator for Multimodal Object Detection](https://mlsanity.com/paper/2608.10724/) (2026) - [Aligned Radiometric RGB-Thermal Fusion for UAV Facade Anomaly Screening](https://mlsanity.com/paper/2609.12521/) (2026) - [Stealthy in Semantics, Antagonistic in Space: Attacking Visible-Infrared Object Detectors via Object-Level Misalignment](https://mlsanity.com/paper/2609.18133/) (2026) - [FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection](https://mlsanity.com/paper/2609.29125/) (2026) ## Abstract (arXiv) Single-modal object detection tasks often experience performance degradation when encountering diverse scenarios. In contrast, multimodal object detection tasks can offer more comprehensive information about object features by integrating data from various modalities. Current multimodal object detection methods generally use various fusion techniques, including conventional neural networks and transformer-based models, to implement feature fusion strategies and achieve complementary information. However, since multimodal images are captured by different sensors, there are often misalignments between them, making direct matching challenging. This misalignment hinders the ability to establish strong correlations for the same object across different modalities. In this paper, we propose a novel approach called the CrOss-Mamba interaction and Offset-guided fusion (COMO) framework for multimodal object detection tasks. The COMO framework employs the cross-mamba technique to formulate feature interaction equations, enabling multimodal serialized state computation. This results in interactive fusion outputs while reducing computational overhead and improving efficiency. Additionally, COMO leverages high-level features, which are less affected by misalignment, to facilitate interaction and transfer complementary information between modalities, addressing the positional offset challenges caused by variations in camera angles and capture times. Furthermore, COMO incorporates a global and local scanning mechanism in the cross-mamba module to capture features with local correlation, particularly in remote sensing images. To preserve low-level features, the offset-guided fusion mechanism ensures effective multiscale feature utilization, allowing the construction of a multiscale fusion data cube that enhances detection performance.