Length-Controllable Image Captioning
Table 1: Performance of the length-aware version of AoANet and VLP on MS COCO Karpathy’s test split. S, C, M and B@N, are short for SPICE, CIDEr-D, METEOR, and BLEU@N scores, respectively. The original results of AoANet and VLP are obtained from models trained by ourselves with the official codes and settings provided by the authors. All values are reported as a percentage (%).
['Metrics Models', 'Metrics Models', 'S AoANet', 'C AoANet', 'M AoANet', 'B@4 AoANet', 'S VLP', 'C VLP', 'M VLP', 'B@4 VLP']
We train length-aware AoANet and VLP following their original training settings. During evaluation, we run our length-aware models on the test split multiple times, where each time we feed in a different length level embedding to generate the captions within different length ranges. From the table, on the normal length range (10-14 tokens), our 4-Level and 5-Level version of VLP and AoANet both achieve competitive or better performance than the original version. Our 4-level VLP even improve the CIDEr-D score by 1.8% over the original VLP. Note that, most of the image captions generated by original AoANet and VLP are inside the length range [10,14], which indicates that our length-aware versions can maintain or even boost the performance of the original models on a normal length range.