Hierarchy Parsing for Image Captioning
Table 3: An ablation study on the use of different features.
['Regions', 'Instances', 'Tree-LSTM', 'BLEU@4', 'METEOR', 'CIDEr-D']
Ablation Study. Next, we examine how captioning performance is affected when capitalizing on different features. The use of original region-level features in general achieves a good performance. As expected, only utilizing original instance-level features is inferior to region-level features. The result indicates that the context in the background of a region is still a complement to the foreground. On a throughout hierarchy parsing of an image, the finer features produced by Tree-LSTM lead to better performance. The concatenation of every two features constantly outperforms the individual one. The integration of all three features, i.e., our HIP, reaches the highest performance for captioning. The results basically demonstrate the complementarity in between.