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Poster
Deep Neural Networks for Object Detection
Christian Szegedy · Alexander Toshev · Dumitru Erhan

Sun Dec 08 02:00 PM -- 06:00 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

Deep Neural Networks (DNNs) have recently shown outstanding performance on the task of whole image classification. In this paper we go one step further and address the problem of object detection -- not only classifying but also precisely localizing objects of various classes using DNNs. We present a simple and yet powerful formulation of object detection as a regression to object masks. We define a multi-scale inference procedure which is able to produce a high-resolution object detection at a low cost by a few network applications. The approach achieves state-of-the-art performance on Pascal 2007 VOC.

Author Information

Christian Szegedy (Google)

Christian Szegedy is a Machine Learning scientist at Google Research. He has a PhD in Mathematics from the University of Bonn, Germany. His most influential past works include the discovery of adversarial examples and various computer vision architectures for image recognition and object detection. He is the co-inventor of Batch-normalization. He is currently working on automated theorem proving and auto-formalization of mathematics via deep learning.

Alexander Toshev (Google)
Dumitru Erhan (Google)

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