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SYNTHIA-AL is a dataset for active learning purposes containing video streams generated at 25 FPS. The classes considered in this dataset are void, sky, car, pedestrian, bycicle, building, road, sidewalk, fence, vegetation, traffic sign, lanemarking, and traffic light. The provided ground truth includes instance segmentation, 2D bounding boxes, 3D bounding boxes and depth information!
For further details, please consult the following README.
Link to download dataset
Short description of portfolio item number 1
Short description of portfolio item number 2
Develope cutting edge deep learning methods for identifying the complex objects in the waste stream. Our highly accurate with low latency solutions incorporating artificial intelligence in the recycling industry are now deployed in many United States and Europe facilities.
Introducing a novel active learning technique based on deep learning for object detection in videos that significantly reduces the annotation effort.
Develope techniques for the image translation by using Generative Adversarial Networks to maximize the visibility in the darkness.
Published in ICCV Workshops, 2019
Published in Journal of SN Computer Science, 2020
Published in CAIP, 2021
Published in WACV, 2021
Published in ICCV Workshops, 2021
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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