Summary of Adaflow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control, by Fenmin Wu et al.
AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control
by Fenmin Wu, Sicong Liu, Kehao Zhu, Xiaochen Li, Bin Guo, Zhiwen Yu, Hongkai Wen, Xiangrui Xu, Lehao Wang, Xiangyu Liu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 A novel approach to distributed sensing tasks in mobile devices is proposed, addressing the challenge of asynchronous data arrival from various sensors such as LiDAR and cameras. The presented method, AdaFlow, formulates structured cross-modality affinity using a hierarchical analysis-based normalized matrix, accommodating the diversity and dynamics of modalities. This enables flexible data imputation and adaptation to various modalities and downstream tasks without retraining. Compared to existing methods, AdaFlow significantly reduces inference latency by up to 79.9% and enhances accuracy by up to 61.9%. The proposed approach is particularly relevant for applications such as smart cabins and driving assistance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mobile devices with sensors like LiDAR and cameras are making it possible to do lots of cool things, like make self-driving cars or monitor environmental conditions. But there’s a problem – the data from these sensors doesn’t always arrive at the same time. This can cause delays or make the information less accurate. To solve this issue, researchers have come up with a new way to process this data called AdaFlow. It helps devices figure out how different types of sensor data relate to each other and use that information to fill in gaps where there’s missing data. This makes it possible for devices to make decisions faster and more accurately. |
Keywords
* Artificial intelligence * Inference
