UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning
Table 4: Experimental results of NIC - Show and Tell models
['Dataset', 'Tokenizer', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE-L', 'CIDEr-D']
The two models are trained with three mentioned datasets, which are English-sportball, GT-sportball, UIT-ViIC. After training, 924 images from validation subset for each dataset are used to validate the our models. However, as number of consecutive words considered (BLEU gram) increase, UIT-ViIC’s BLEU scores start to pass that of English sportball and their gaps keep growing. The ROUGE-L and CIDEr-D scores for UIT-ViIC model prove the same thing, and interestingly, we can observe that the CIDEr-D score for the UIT-ViIC model surpasses English-sportball counterpart. Show and Tell model’s results show that MS-COCO sportball English captions only gives better result at BLEU-1. From BLEU-3 to BLEU-4, both GT-sportball and UIT-ViIC yield superior scores to English-sportball. Besides, when limiting MS-COCO English dataset to sportball category only, the results are higher (0.689, 0.501, 0.355, 0.252) than when the model is trained on MS-COCO with all images, which scored only 0.629, 0.436, 0.290, 0.193 (results without tuning in 2018) from BLEU-1 to BLEU-4 respectively.