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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/cs-seminar-vishal-patel-johns-hopkins-university-deep-networks-for-open-set-visual-recognition/embed/" width="600" height="338" title="&#x201C;CS Seminar: Vishal Patel, Johns Hopkins University &#x2013; &#x201C;Deep Networks for Open Set Visual Recognition&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><description>LocationHackerman Hall B-17AbstractOver the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as&hellip;</description></oembed>
