SBAT: Video Captioning with Sparse Boundary-Aware Transformer
Table 2: Evaluation results of video captioning, where B, R, M, C denote BLEU4, ROUGE, METEOR, CIDEr, respectively.
['Dataset', 'Method', 'B', 'R', 'M', 'C']
For a fair comparison, we compare SBAT with the methods which also use image features and motion features. On MSR-VTT, SBAT outperforms TVT, MGSA, Dense Cap, MARN, GRU-EVE, POS-CG on all the metrics. In particular, SBAT achieves 51.6% on CIDEr, making an improvement of 2.6% over POS-CG. On MSVD, SBAT outperforms SCN, GRU-EVE, POS-CG on all the metrics and has a better overall performance than TVT and MARN.