Summary of Como: Cross-mamba Interaction and Offset-guided Fusion For Multimodal Object Detection, by Chang Liu et al.
COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal Object Detection
by Chang Liu, Xin Ma, Xiaochen Yang, Yuxiang Zhang, Yanni Dong
First submitted to arxiv on: 24 Dec 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 The proposed COMO framework is designed for multimodal object detection tasks, which typically integrate information from various modalities to provide more comprehensive object features. By employing cross-mamba interaction and offset-guided fusion, COMO enables serialized state computation and interactive fusion outputs while reducing computational overhead. The framework also incorporates high-level features to facilitate information transfer between modalities, addressing positional offset challenges caused by varying camera angles and capture times. Additionally, COMO includes a global and local scanning mechanism for capturing remote sensing image features with local correlation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COMO is a new approach for multimodal object detection that helps machines detect objects better when combining data from different sources. This method uses a special technique to combine information from multiple sensors, like cameras or radar, and adjusts for differences in how these sensors capture images. COMO does this by using high-level features, which are less affected by changes in camera angles and timing, and by incorporating scanning mechanisms that help capture features with local connections. |
Keywords
» Artificial intelligence » Object detection
