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Automatic video captioning is a challenging multimodal task requiring the successful combination of computer vision and natural language generation. This task has many challenges, with evaluation of the task itself being one of the most problematic. Video captioning differs from other tasks by looking for accurate reports of specific objects and needing to produce a description of the context. A significant challenge in evaluating video captioning systems lies in the fact that there legitimately exists multiple. Often many distinct ways to describe what took place in a single video. The usual automatic metrics used for video captioning are borrowed from other tasks like machine translation or image captioning. However, they fail to include the context of the scene. Validating the accuracy of a given metric is ordinarily carried out by correlating it with the human assessment of the quality of system-generated captions. In this paper, we present a new automatic metric that directly learns from human evaluations to maximize the correlation with human judgement.




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