Summary of Temporal Embeddings: Scalable Self-supervised Temporal Representation Learning From Spatiotemporal Data For Multimodal Computer Vision, by Yi Cao and Swetava Ganguli and Vipul Pandey
Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision
by Yi Cao, Swetava Ganguli, Vipul Pandey
First submitted to arxiv on: 16 Oct 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 novel self-supervised approach stratifies landscapes based on mobility activity time series by transforming the signal into the frequency domain and compressing it into task-agnostic temporal embeddings. The pixel-wise embeddings are converted to image-like channels for deep semantic segmentation of geospatial tasks, such as classifying residential and commercial areas. Experiments show that temporal embeddings preserve cyclic patterns and are effective across different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a connection between the patterns of movement over time and the type of land use. It creates a new way to group landscapes based on how people move around using time series data. The method involves taking the time series signal, turning it into a frequency domain, and then shrinking it down into a smaller representation that keeps the important temporal patterns. This small representation can be used for tasks like classifying areas as residential or commercial. The results show that this method is useful for different tasks. |
Keywords
* Artificial intelligence * Self supervised * Semantic segmentation * Time series
