A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
Table 2: Comparison to state-of-the-art proposal generation methods for dense video captioning task on the validation set of ActivityNet Captions. Results are reported for Precision, Recall, & F1-measure. The best and 2nd best results are highlighted.
['[EMPTY]', '[BOLD] Full Dataset  [BOLD] was Available', '[BOLD] Prec.', '[BOLD] Rec.', '[BOLD] F1']
The results of the comparison are presented in Tab. We highlight that our model was trained on only 91 % of the number of videos other models were trained on as they are no longer available on YouTube. Nevertheless, our proposal generation model achieves the state-of-the-art performance on F1 metric. Specifically, our model provides good ground truth segment coverage while being quite accurate in its predictions.