Summary of Cross-view Transformers For Real-time Map-view Semantic Segmentation, by Brady Zhou et al.
Cross-view Transformers for real-time Map-view Semantic Segmentation
by Brady Zhou, Philipp Krähenbühl
First submitted to arxiv on: 5 May 2022
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Our research presents a novel attention-based model for map-view semantic segmentation from multiple cameras. We developed cross-view transformers that efficiently learn a mapping between individual camera views and a canonical map-view representation using a camera-aware cross-view attention mechanism. The architecture combines convolutional image encoders with transformer layers to infer map-view semantic segmentation, allowing for real-time processing and state-of-the-art performance on the nuScenes dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a special model that can see multiple cameras at once and create a single map of what’s happening. It works by using something called attention, which helps it figure out how to match things from each camera together. We also used special codes in each camera’s picture that help the computer understand where things are and what they’re doing. This model is very fast and good at its job, beating other models on a big test dataset. |
Keywords
* Artificial intelligence * Attention * Semantic segmentation * Transformer
