Fusion-Mamba for Cross-Modality Object Detection

Cross-modality object detection aims to fuse complementary information from different modalities to improve model performance, which achieves a wider range of applications. However, traditional cross-modality fusion methods, based on CNN or Transformer, inadequately address the issue of pseudo-target information, which causes model attention dispersion to degrade object detection performance. In this paper, we investigate a novel cross-modality fusion approach by associating cross-modal features in a hidden state space based on an improved Mamba with a gating attention mechanism. We propose the Fusion-Mamba Block(FMB), designed to map cross-modal features into a hidden state space for interaction, thereby…

• MO
  Ministry of Education, Libya

  Award: KLATASDS2305
• NN
  National Natural Science Foundation of China

  Award: 62102151
• NS
  Natural Science Foundation of Beijing Municipality

  Awards: L223024, L244043, Z241100001324017