Multi-modal Dense Video Captioning
Table 1: The results of the dense video captioning task on the ActivityNet Captions validation sets in terms of BLEU–3,4 (B@3, B@4) and METEOR (M). The related methods are compared with the proposed approach (MDVC) in two settings: on the full validation dataset and a part of it with the videos with all modalities present for a fair comparison (“no missings”). Methods are additionally split into the ones which “saw” all training videos and another ones which trained on partially available data. The results are presented for both ground truth (GT) and learned proposals.
['Method', 'GT Proposals B@3', 'GT Proposals B@4', 'GT Proposals M', 'Learned Proposals B@3', 'Learned Proposals B@4', 'Learned Proposals M']
The obtained results are presented in Tab. Our method (MDVC) achieves comparable or better performance, even though we have access to smaller training set and 9 % of the validation videos are missing (replaced with random input features). Furthermore, if all three modalities are present, our method outperforms all baseline approaches in the case of both GT and learned proposals. This shows the superior performance of our captioning module which, yet, trained on the smaller amount of data.