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SYNTHIA-AL : The Autonomous Driving Dataset

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

Portfolio item number 1

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AI for Recycling

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.

AI for Autonomous Driving

Introducing a novel active learning technique based on deep learning for object detection in videos that significantly reduces the annotation effort.

Audi Lighting Technology

Develope techniques for the image translation by using Generative Adversarial Networks to maximize the visibility in the darkness.

Temporal Coherence for Active Learning in Videos

Published in ICCV Workshops, 2019

Saliency from High-Level Semantic Image Features

Published in Journal of SN Computer Science, 2020

When Deep Learners Change Their Mind: Learning Dynamics for Active Learning

Published in CAIP, 2021

Class-Balanced Active Learning for Image Classification

Published in WACV, 2021

Reducing Label Effort: Self-Supervised meets Active Learning

Published in ICCV Workshops, 2021

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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