A25 - Computer Vision by Learning

From: February 21 to 27, 2017

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This graduate course is especially meant for Ph.D. students who have basic familiarity with computer vision, image processing, and pattern recognition and want to upsurge their knowledge and machinery to the state-of-the-art, with direct utility in their own research.

The topic of attention is the challenge of computer vision by learning.

We address the theoretical foundations of computer vision in conjunction with machine learning and present algorithms that achieve state-of-the-art performance while maintaining efficient execution with minimal supervision. This year we explain and emphasize on computer vision by deep learning, including challenges like image classification by convolutional neural networks, object tracking by Siamese networks, action recognition with attention LSTMs, and event recognition by video embeddings. We give an overview of the latest developments and future trends in the field on the basis of several recent challenges, including the ImageNet and TRECVID benchmarks, the leading competitions for visual search engines based on computer vision by learning, and we indicate how to obtain improvements in the near future.

The course will feature an invited tutorial by Laurens van der Maaten from Facebook AI Research, New York.


Dates: Wednesday Feb 22 to Tuesday Feb 28 2017
Location: University of Amsterdam








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