RGBX-DiffusionDet: 一种利用DiffusionDet进行多模态RGB-X目标检测的框架
RGBX-DiffusionDet: A Framework for Multi-Modal RGB-X Object Detection Using DiffusionDet
AI总结：
RGBX-DiffusionDet通过自适应多模态编码器融合RGB与X数据，提升多模态目标检测性能，实现高效且紧凑的特征嵌入。
摘要：
本文介绍了RGBX-DiffusionDet，一种扩展DiffusionDet模型以通过自适应多模态编码器融合异构2D数据（X）与RGB图像的物体检测框架。为了实现跨模态交互，我们设计了卷积块注意力模块中的动态通道减少（DCR-CBAM），该模块通过动态突出显著通道特征来促进子网络之间的交互。此外，提出了动态多级聚合块（DMLAB），通过自适应多尺度融合来细化空间特征表示。最后，引入了新的正则化损失，强制通道显著性和空间选择性，导致紧凑且具有辨别性的特征嵌入。通过RGB-Depth（KITTI）、一个新颖的标注RGB-偏振数据集和RGB-红外（M³FD）基准数据集进行了大量实验。我们展示了所提出方法在基准RGB-only DiffusionDet上的持续优越性。模块化架构保持了原始解码复杂度，确保了效率。这些结果确立了所提出的RGBX-DiffusionDet作为一种灵活的多模态目标检测方法，为将不同2D传感模态整合到扩散检测管道中提供了新的见解。
英文摘要：
This work introduces RGBX-DiffusionDet, an object detection framework extending the DiffusionDet model to fuse the heterogeneous 2D data (X) with RGB imagery via an adaptive multimodal encoder. To enable cross-modal interaction, we design the dynamic channel reduction within a convolutional block attention module (DCR-CBAM), which facilitates cross-talk between subnetworks by dynamically highlighting salient channel features. Furthermore, the dynamic multi-level aggregation block (DMLAB) is proposed to refine spatial feature representations through adaptive multiscale fusion. Finally, novel regularization losses that enforce channel saliency and spatial selectivity are introduced, leading to compact and discriminative feature embeddings. Extensive experiments using RGB-Depth (KITTI), a novel annotated RGB-Polarimetric dataset, and RGB-Infrared (M$^3$FD) benchmark dataset were conducted. We demonstrate consistent superiority of the proposed approach over the baseline RGB-only DiffusionDet. The modular architecture maintains the original decoding complexity, ensuring efficiency. These results establish the proposed RGBX-DiffusionDet as a flexible multimodal object detection approach, providing new insights into integrating diverse 2D sensing modalities into diffusion-based detection pipelines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
