Summary of Fusion-mamba For Cross-modality Object Detection, by Wenhao Dong et al.
Fusion-Mamba for Cross-modality Object Detection
by Wenhao Dong, Haodong Zhu, Shaohui Lin, Xiaoyan Luo, Yunhang Shen, Xuhui Liu, Juan Zhang, Guodong Guo, Baochang Zhang
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative approach to improve object detection performance by fusing complementary information from different modalities, such as images with varying camera angles and focal lengths. The proposed method, dubbed Fusion-Mamba block (FMB), is built upon an improved Mamba architecture with a gating mechanism that enables the association of cross-modal features in a hidden state space. FMB consists of two modules: State Space Channel Swapping (SSCS) for shallow feature fusion and Dual State Space Fusion (DSSF) for deep fusion in a hidden state space. Experimental results on public datasets demonstrate the superiority of the proposed approach over state-of-the-art methods, achieving an mAP of 5.9% on M^3FD and 4.9% on FLIR-Aligned datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better detect objects by combining information from different camera views. Imagine taking a picture of a car with a smartphone, then looking at the same car from a drone’s perspective. The proposed method brings these two views together to improve object detection performance. It does this by creating a special “hidden state space” where features from both modalities can interact and become more consistent. This approach is called Fusion-Mamba block (FMB). FMB has two parts: one that helps fuse shallow features and another that fuses deeper features in the hidden state space. In experiments, this method outperforms other methods by a significant margin. |
Keywords
» Artificial intelligence » Object detection
