MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters

• URL: http://arxiv.org/abs/2512.00363v1
• Date: Sat, 29 Nov 2025 07:23:01 GMT
• Title: MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters
• Authors: Jianhong Han, Yupei Wang, Yuan Zhang, Liang Chen,
• Abstract summary: Multimodal remote sensing object detection aims to achieve more accurate and robust perception under challenging conditions.<n>Existing approaches that rely on attention-based or deformable convolution fusion blocks still struggle to balance performance and lightweight design.<n>We propose MM-DETR, a lightweight and efficient framework for multimodal object detection.
• Score: 12.063966356953186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal remote sensing object detection aims to achieve more accurate and robust perception under challenging conditions by fusing complementary information from different modalities. However, existing approaches that rely on attention-based or deformable convolution fusion blocks still struggle to balance performance and lightweight design. Beyond fusion complexity, extracting modality features with shared backbones yields suboptimal representations due to insufficient modality-specific modeling, whereas dual-stream architectures nearly double the parameter count, ultimately limiting practical deployment. To this end, we propose MM-DETR, a lightweight and efficient framework for multimodal object detection. Specifically, we propose a Mamba-based dual granularity fusion encoder that reformulates global interaction as channel-wise dynamic gating and leverages a 1D selective scan for efficient cross-modal modeling with linear complexity. Following this design, we further reinterpret multimodal fusion as a modality completion problem. A region-aware 2D selective scanning completion branch is introduced to recover modality-specific cues, supporting fine-grained fusion along a bidirectional pyramid pathway with minimal overhead. To further reduce parameter redundancy while retaining strong feature extraction capability, a lightweight frequency-aware modality adapter is inserted into the shared backbone. This adapter employs a spatial-frequency co-expert structure to capture modality-specific cues, while a pixel-wise router dynamically balances expert contributions for efficient spatial-frequency fusion. Extensive experiments conducted on four multimodal benchmark datasets demonstrate the effectiveness and generalization capability of the proposed method.

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