Learning Question-Guided Video Representation for Multi-Turn Video Question Answering
Table 2: Comparison with existing approaches: Naïve Fusion Alamri et al. (2019b); Zhuang et al. (2019), Attentional Fusion Hori et al. (2018); Zhuang et al. (2019), Multi-Source Sequence-to-Sequence model (Pasunuru and Bansal, 2019), Modified Attentional Fusion with Maximum Mutual Information objective Zhuang et al. (2019) and Hierarchical Attention with pre-trained embedding (Le et al., 2019), on the AVSD public test set. For each approach, we report its corpus-wide scores on BLEU-1 through BLEU-4, METEOR, ROUGE-L and CIDEr. We report the mean and standard deviation scores of 5 runs using random initialization and early stopping on the public (prototype) validation set.
['[BOLD] Single-Turn VideoQA Models', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'METEOR', 'ROUGE-L', 'CIDEr']
We evaluate our proposed approach using the same natural language generation evaluation toolkit NLGEval Sharma et al. The corpus-wide scores of the following unsupervised automated metrics are reported, including BLEU-1 through BLEU-4 Papineni et al. We report the mean and standard deviation scores of 5 runs using random initialization and early stopping on the public (prototype) validation set. We apply our model in two scenarios: single-turn and multi-turn VideoQA. The only difference is that in single-turn VideoQA, the dialogue context encoder is excluded from the model.