[
Table 2: Comparison with the existing models on MSR-VTT.
['Model', 'B-4', 'C', 'M', 'R', 'Overall']
Empirically, we evaluate our method on Youtube2Text/MSVD (guadarrama2013youtube2text) and MSR-VTT (xu2016msr-vtt). v2t_navigator, Aalto, VideoLAB are the top 3 models in MSR-VTT 2017 challenge. MTVC and SibNet are similar to the ones trained on MSVD. CIDEnt-RL optimizes the model with entailment-enhanced reward (CIDEnt) by reinforcement learning technique. The CIDEr of our method is only 0.3 lower than CIDEnt-RL which directly optimizes CIDEr by RL method. And our method is better than CIDEnt-RL on other metrics by at least 1.6%. HACA exploits a so-called hierarchically aligned cross-modal attention framework to fuse multi-modal features both spatially and temporally. Our model outperforms HACA on all metrics except for METEOR which is lower by 2%. TAMoE takes advantage of external corpus and composes several experts based on external knowledge to generate captions for video.