DETR is Secretly a Multispectral Detector: Zero-Parameter Adaptation via Semantic Alignment

Multispectral object detection enhances perceptual robust-ness in challenging environments by fusing complementary informationfrom RGB and infrared modalities. However, existing methods predomi-nantly follow a parameter-expanding paradigm, introducing customizedfusion modules that inevitably increase computational overhead and dis-rupt the topological consistency with unimodal pre-trained weights. Inthis paper, we challenge this convention and propose ZPA-MDETR,a zero-parameter adaptive multispectral framework without additionallearnable fusion parameters. By re-examining the DETR architecture,we reveal that its inherent query-memory interaction mechanism pro-vides a natural foundation for cross-modal fusion. Specifically, ZPA-MDETR strictly preserves the architecture of the unimodal DETR andachieves flexible and effective fusion via an asymmetric input organiza-tion strategy, where one modality serves as the memory, and the otherinitializes object queries. To bridge the cross-modal distribution dis-crepancy, we introduce parameter-free semantic alignment constraintsduring training. Extensive experiments on four public datasets acrossdifferent scenes demonstrate that ZPA-MDETR outperforms state-of-the-art methods while maintaining the same parameter scale as theunimodal framework, establishing a favorable trade-off between detec-tion accuracy and deployment efficiency. The source code is available athttps://github.com/UserXiangYang/ZPA-MDETR.