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Deep Neural Networks Do Not Recognize Negative Images

March 22, 2017

Deep Neural Networks Do Not Recognize Negative Images

Deep Neural Networks have achieved remarkable performance on a variety of pattern-recognition tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. Unlike computers, humans have an overall sense of the objects and can recognize them in various forms such as different scales, orientations, colors or brightness. Since […]

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August 29, 2016

Fashion! Turn to the left! Computational model for visual stimuli

Clothing fashions are usually expressed with visual stimuli, for example style, color or texture. A collaboration of researchers from Shenzhen University, Sun Yat-sen University and University of South Carolina decided to use machine learning and computer vision techniques to see which visual stimulus has higher influence on updating of clothing fashion. They have proposed a […]

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August 19, 2016

Seeing with Humans: Gaze-Assisted Neural Image Captioning

Gaze tracking uses human gaze data for various applications in computer-vision systems, since gaze reflects how humans process visual scenes. Recently, researchers from Max Planck Institute for Informatics, Saarbrucken wanted to know whether gaze data can also be beneficial for scene-centric tasks, such as image captioning. They presented a new perspective on gaze-assisted image captioning […]

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August 16, 2016

Enabling My Robot To Play Pictionary

The most approaches for hand-drawn sketch recognition usually ignore the sequential aspect of freehand sketching, or exploit it ad hoc. Researchers from the Indian Institute of Science have recently proposed a recurrent neural network architecture for sketch-object recognition. Their method exploits the long-term sequential and structural regularities in stroke data. Their framework is inherently online […]

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July 1, 2016

A computer reads a story

Sequencing is a task for children that aims to improve understanding of the temporal occurrence in a sequence of events, so that they sort various images (sometimes with captions) into a coherent story. Researchers from Virginia Tech and TTI Chicago have proposed the task of machine-learning sequencing – given a jumbled set of aligned image-caption […]

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