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However, evaluating the quality\nof descriptions has proven to be challenging. We propose a novel paradigm for\nevaluating image descriptions that uses human consensus. This paradigm consists\nof three main parts: a new triplet-based method of collecting human annotations\nto measure consensus, a new automated metric (CIDEr) that captures consensus,\nand two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences\ndescribing each image. Our simple metric captures human judgment of consensus\nbetter than existing metrics across sentences generated by various sources. We\nalso evaluate five state-of-the-art image description approaches using this new\nprotocol and provide a benchmark for future comparisons. A version of CIDEr\nnamed CIDEr-D is available as a part of MS COCO evaluation server to enable\nsystematic evaluation and benchmarking.","url_abs":"http://arxiv.org/abs/1411.5726v2","url_pdf":"http://arxiv.org/pdf/1411.5726v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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