Summary of Mm-path: Multi-modal, Multi-granularity Path Representation Learning — Extended Version, by Ronghui Xu et al.
MM-Path: Multi-modal, Multi-granularity Path Representation Learning – Extended Version
by Ronghui Xu, Hanyin Cheng, Chenjuan Guo, Hongfan Gao, Jilin Hu, Sean Bin Yang, Bin Yang
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Multi-modal, Multi-granularity Path Representation Learning Framework (MM-Path) aims to develop effective path representations by integrating information from multiple modalities and granularities. This framework learns a generic path representation by fusing road networks and image-based data. A multi-granularity alignment strategy is developed to synchronize local and global contexts across different modalities. Additionally, a graph-based cross-modal residual fusion component is introduced to effectively handle heterogeneity in multi-modal data. The proposed MM-Path is validated through extensive experiments on two large-scale real-world datasets under various downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of learning path representations by combining information from different sources like road networks and images. This helps create more accurate and generalizable models that can understand both local and global details. The authors develop a special framework to align these different types of data and then use it to learn about paths in a way that’s useful for tasks like route planning. |
Keywords
» Artificial intelligence » Alignment » Multi modal » Representation learning
