Summary of Alert-transformer: Bridging Asynchronous and Synchronous Machine Learning For Real-time Event-based Spatio-temporal Data, by Carmen Martin-turrero et al.
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
by Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein, Pietro Zanuttigh, Vincent Parret
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 hybrid pipeline combines classic processing of continuous ultra-sparse spatiotemporal data from event-based sensors with dense machine learning models, enabling efficient and timely object and gesture recognition. The ALERT module uses PointNet models to continuously integrate new events while dismissing old ones, allowing for flexible readout of embedded data at any sampling rate. This is achieved through a patch-based approach inspired by Vision Transformer, optimized for input sparsity. A transformer model trained for object and gesture recognition processes the embeddings, achieving state-of-the-art performances with lower latency than competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to process information from special sensors that capture events in space and time. They want to use this information to recognize objects and gestures quickly and accurately. To do this, they combined two methods: one that learns about the sensor data and another that uses this knowledge to identify objects and gestures. This combination allows for fast and efficient recognition at any desired speed. |
Keywords
* Artificial intelligence * Gesture recognition * Machine learning * Spatiotemporal * Transformer * Vision transformer
