Summary of Encoding Agent Trajectories As Representations with Sequence Transformers, by Athanasios Tsiligkaridis et al.
Encoding Agent Trajectories as Representations with Sequence Transformers
by Athanasios Tsiligkaridis, Nicholas Kalinowski, Zhongheng Li, Elizabeth Hou
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Transformer-based neural network architecture is proposed to represent high-dimensional spatiotemporal trajectories as sequences of discrete locations, enabling learning of representations and structure through both supervised and self-supervised tasks. The Sequence Transformer for Agent Representation Encodings (STARE) model can learn meaningful encodings useful for downstream tasks such as classification and similarity detection on various synthetic and real trajectory datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to understand and analyze complex movements like people walking in cities or animals roaming in forests. This paper proposes a new way to do just that by using artificial intelligence techniques similar to those used for language processing. The goal is to learn patterns and relationships from data about where things are and how they move over time. This could help us better understand and predict complex behaviors like traffic flow, social interactions, or animal migrations. |
Keywords
» Artificial intelligence » Classification » Neural network » Self supervised » Spatiotemporal » Supervised » Transformer
