Summary of Breaking Data Silos: Cross-domain Learning For Multi-agent Perception From Independent Private Sources, by Jinlong Li et al.
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
by Jinlong Li, Baolu Li, Xinyu Liu, Runsheng Xu, Jiaqi Ma, Hongkai Yu
First submitted to arxiv on: 6 Feb 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 This paper investigates the impact of the Distribution Gap on multi-agent perception systems. In these systems, agents from different companies may use identical encoders for feature extraction, but train on private and independent data sources. This gap can lead to significant performance declines. The authors introduce a Feature Distribution-aware Aggregation (FDA) framework to mitigate this gap through two components: Learnable Feature Compensation Module and Distribution-aware Statistical Consistency Module. These modules aim to minimize the distribution gap among multi-agent features, enhancing intermediate features for better 3D object detection in point clouds. Experiments on OPV2V and V2XSet datasets demonstrate FDA’s effectiveness as a valuable augmentation to existing systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers look at how different companies’ private data affects their artificial agents that work together to understand the world around them. They found that when these agents are trained separately using their own private data, they don’t work well together. The authors create a new way to help these agents work better by sharing information and reducing differences between their training data. This helps them detect objects more accurately in 3D environments. |
Keywords
* Artificial intelligence * Feature extraction * Object detection
