Summary of Cocoon: Robust Multi-modal Perception with Uncertainty-aware Sensor Fusion, by Minkyoung Cho et al.
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion
by Minkyoung Cho, Yulong Cao, Jiachen Sun, Qingzhao Zhang, Marco Pavone, Jeong Joon Park, Heng Yang, Z. Morley Mao
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Cocoon framework is an object- and feature-level uncertainty-aware fusion method for 3D object detection that addresses limitations in existing approaches. By introducing a feature aligner and learnable surrogate ground truth called feature impression, Cocoon enables fair comparison across modalities and quantifies uncertainties arising from distinct object configurations. The framework consistently outperforms existing methods in both normal and challenging conditions, including those with natural and artificial corruptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cocoon is a new way to combine different views of objects in 3D space to improve detection accuracy. It helps by understanding how certain the predictions are for each object. This makes it better at detecting objects in different situations and with different types of noise or distortion. Cocoon works well on various datasets and is more accurate than previous methods. |
Keywords
» Artificial intelligence » Object detection
