Describing Multimedia Content using Attention-based Encoder–Decoder Networks
TABLE III: The performances of the image caption generation models in the Microsoft COCO Image Captioning Challenge. (⋆) [20], (∙) [18], (∘) [45], (⋄) [46] and (∗) [22]. The rows are sorted according to M1.
['Model', 'Human M1', 'Human M2', 'Automatic BLEU', 'Automatic CIDEr']
In this Challenge, the attention-based approach ranked third based on the percentage of captions that are evaluated as better or equal to human caption (M1) and the percentage of captions that pass the Turing Test (M2). Interestingly, the same model was ranked eighth according to the most recently proposed metric of CIDEr and ninth according to the most widely used metric of BLEU. It means that this model has better relative performance in terms of human evaluation than in terms of the automatic metrics, which only look at matching subsequences of words, not directly at the meaning of the generated sentence.