mlsanityData to 14 Oct 2026

HiddenObject: Modality-Agnostic Fusion for Multimodal Hidden Object Detection

arXiv 2508.21135 ↗First posted 28 Aug 2025cs.CV cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Point clouds, detection and remote sensing

Abstract (arXiv)

Detecting hidden or partially concealed objects remains a fundamental challenge in multimodal environments, where factors like occlusion, camouflage, and lighting variations significantly hinder performance. Traditional RGB-based detection methods often fail under such adverse conditions, motivating the need for more robust, modality-agnostic approaches. In this work, we present HiddenObject, a fusion framework that integrates RGB, thermal, and depth data using a Mamba-based fusion mechanism. Our method captures complementary signals across modalities, enabling enhanced detection of obscured or camouflaged targets. Specifically, the proposed approach identifies modality-specific features and fuses them in a unified representation that generalizes well across challenging scenarios. We validate HiddenObject across multiple benchmark datasets, demonstrating state-of-the-art or competitive performance compared to existing methods. These results highlight the efficacy of our fusion design and expose key limitations in current unimodal and na"ive fusion strategies. More broadly, our findings suggest that Mamba-based fusion architectures can significantly advance the field of multimodal object detection, especially under visually degraded or complex conditions.

Key facts. No citing papers in the map yet (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 62 of its 85 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

62 of 85 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Mamba YOLO: A Simple Baseline for Object Detection with State Space Model9 Jun
  2. 2024FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba15 Apr
  3. 2024Fusion-Mamba for Cross-modality Object Detection14 Apr

Other references · 59

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  2. 2020End-to-End Object Detection with Transformerscited by 7,414
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
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  9. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
  10. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,056
  11. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  12. 2024VMamba: Visual State Space Modelcited by 969
38 more
  1. 2022Simplified State Space Layers for Sequence Modelingcited by 788
  2. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 608
  3. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 573
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  2. 2025LEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detection
  3. 2024COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal Object Detection
  4. 2024DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
  5. 2025MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters
  6. 2025WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection
  7. 2026MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detection
  8. 2026M2I2HA: Multi-modal Object Detection Based on Intra- and Inter-Modal Hypergraph Attention