Video Captioning via Hierarchical Reinforcement Learning
Table 2: Results on Charades Captions dataset. We reported BLEU (B), METEOR (M), ROUGH-L (R) and CIDEr (C) scores of our HRL method and two baselines for comparison.
['Method', 'B@1', 'B@2', 'B@3', 'B@4', 'M', 'R', 'C']
Since there were no other papers reporting results on Charades Captions, we mainly compared our HRL model with our implementation of XE-baseline and RL-baseline. Meanwhile, we explored the dimension of the latent goal vector (We used HRL-X to denote the HRL model with a goal dimension of X). Note that our HRL model achieved bigger improvement over the baseline methods on Charades Captions dataset than on MSR-VTT. Given that fact that the average cation length of Charades Captions was much longer than that of MST-VTT (24.13 vs 9.28 words), the difference of the improvement gaps demonstrated that our HRL model can gain better improvement on detailed descriptions of longer videos.