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We propose a novel measure of visual similarity for image retrieval that incorporates both structural and aesthetic (style) constraints. Our algorithm accepts a query as sketched shape, and a set of one or more contextual images specifying the desired visual aesthetic. A triplet network is used to learn a feature embedding capable of measuring style similarity independent of structure, delivering...
Learning visual attributes is an effective approach for zero-shot recognition. However, existing methods are restricted to learning explicitly nameable attributes and cannot tell which attributes are more important to the recognition task. In this paper, we propose a unified framework named Grouped Simile Ensemble (GSE). We claim our contributions as follows. 1) We propose to substitute explicit attribute...
Complexity of understanding a visual scene is the single biggest challenge in creating intelligent devices for visually impaired people. The requirement of real time operation makes it inevitable to design algorithms that obey the computing and memory limits of available hardware. We present a hierarchical scene understanding system implemented on a vision system chip. It is restricted to extract...
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