Learning to name objects

Vicente Ordonez, Wei Liu, Jia Deng, Yejin Choi, Alexander C. Berg, Tamara L. Berg
<span title="2016-02-25">2016</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i6iajiiyxvgz3ob6jwcn2pufni" style="color: black;">Communications of the ACM</a> </i> &nbsp;
We have seen remarkable recent progress in computational visual recognition, producing systems that can classify objects into thousands of different categories with incre asing accuracy. However, one question that has received relatively less attention is "what labels should recognition systems output?" This paper looks at the problem of predicting category labels that mimic how human observers would name objects. This goal is related to the concept of entry-level categories first introduced by
more &raquo; ... psychologists in the 1970s and 1980s. We extend these seminal ideas to study human naming at large scale and to learn computational models for predicting entry-level categories. Practical applications of this work include improving human-focused computer vision applications such as automatically generating a natural language description for an image or textbased image search.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2885252">doi:10.1145/2885252</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lucvnvy4qndznghw3hzhjgqzzy">fatcat:lucvnvy4qndznghw3hzhjgqzzy</a> </span>
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