COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval
TABLE IV: Automated evaluation of different models for image tagging. Cascading MLP learned from cross-lingual data is the best.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F-measure']
The proposed Cascading MLP tops the performance. Although the number of training images in COCO-MT is 6.6 times as large as COCO-CN, the MLP trained on COCO-CN outperforms its COCO-MT counterpart with a clear margin. This result shows the importance of high-quality annotation for training. Some image tagging results are presented in Table VII. In the first row, the tag umbrella is not predicted by MLP trained on COCO-CN, while in the second row, MLP trained on COCO-MT incorrectly predicts the tag keyboard. Learning from the two complementary datasets, Cascading MLP makes better prediction in general.