A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
Table 4: The performance of other methods on the filtered ActivityNet Captions validation set for videos which are no longer available (around 91 % (as ours)). The results are reported in the learned proposal setting. As expected, the performance of other models remains at the same level while ours gains the missing 9 %. Metrics are BLEU3–4, METEOR, recall, precision, and F1-measure.
['[EMPTY]', '[BOLD] Validation', '[BOLD] B@3', '[BOLD] B@4', '[BOLD] M', '[BOLD] Recall', '[BOLD] Prec.', '[BOLD] F1']
In other words, we hypothesise that the performance of other methods will not change after excluding videos from both predictions and ground truth while the performance of our method will be higher by around 9 % (a portion of the missing videos). The results of the comparison are shown in Tab. We remind a reader that the compared methods were trained on the full training dataset while ours was trained on only 91 %.