COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval
TABLE VIII: Automated evaluation of image captioning models trained on different datasets. The proposed cross-lingual transfer performs the best.
['[BOLD] Training', '[BOLD] BLEU-4', '[BOLD] METEOR', '[BOLD] ROUGE-L', '[BOLD] CIDEr']
Models trained on Flickr8k-CN and AIC-ICC have fairly low scores. The relatively limited size of Flickr8k-CN makes it lack the ability for training a good captioning model. As for AIC-ICC, although it is the largest Chinese captioning dataset, it is strongly biased that all the images are about human beings. Consequently, the model trained on this dataset is unsuitable for describing general images. The model trained on COCO-CN is on par with the model trained on machine translated sentences of the full MS-COCO set. As for COCO-Mixed, since manually written sentences from COCO-CN are overwhelmed by machine translated sentences from COCO-MT during training, the benefit of COCO-CN appears to be marginal. By contrast, Sequential learning is quite effective, performing best under all metrics.