Are metrics measuring what they should? An evaluation of image captioning task metrics
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Image Captioning is a current research task to describe the image content using the objects and their relationships in the scene. To tackle this task, two important research areas are used, artificial vision, and natural language processing. In Image Captioning, as in any computational intelligence task, the performance metrics are crucial for knowing how well (or bad) a method performs. In recent years, it has been observed that classical metrics based on n-grams are insufficient to capture the semantics and the critical meaning to describe the content in an image. Looking to measure how well or not the set of current and more recent metrics are doing, in this manuscript, we present an evaluation of several kinds of Image Captioning metrics and a comparison between them using the well-known MS COCO dataset. For this, we designed two scenarios; 1) a set of artificially build captions with several quality, and 2) a comparison of some state-of-the-art Image Captioning methods. We tried to answer the questions: Are the current metrics helping to produce high quality captions? How do actual metrics compare to each other? What are the metrics really measuring?
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