Fusion-Mamba for Cross-modality Object Detection

arXiv:2404.09146v1 Announce Type: new
Abstract: Cross-modality fusing complementary information from different modalities effectively improves object detection performance, making it more useful and robust for a wider range of applications. Existing fusion strategies combine different types of images or merge different backbone features through elaborated neural network modules. However, these methods neglect that modality disparities affect cross-modality fusion performance, as different modalities with different camera focal lengths, placements, and angles are hardly fused. In this paper, we investigate cross-modality fusion by …

abstract applications arxiv cs.ai cs.cv detection features fusion however images information making mamba merge modules network neural network object performance robust strategies through type types