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VIVID: Virtual Environment for Visual Deep Learning
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Prof. Lai introduces VIVID: Virtual Environment for Visual Deep Learning in this video. VIVID can be applied in different areas: Semantic Segmentation, Depth Prediction, Autonomous Navigation, Action Recognition. Prof. Lai will explain each of them in detail.
There are certain advantages of VIVID:
- It is easy to use
- Specific API for deep learning
- Distributed learning through TCP/IP
- Simulate real-life events with human actions
- Large-scale indoor and outdoor scenes
