259a5df46308d60f8454bd4adcc3b462-Supplemental-Conference.pdf

As action decoder their mentioned architectures of is multimodal adopted in the in to paper Figure information generate, the 1. visual-gr natural with languages cross-attention ounded alignment conditioned blocks, decoder on while the is visual applied the visual-grounded input. Based on these deeply fused representations, we finally generate the predicted answers with the visual-grounded generation decoder. In this section, we describe the settings used when fine-tuning the pretrained models on various downstream tasks. We use RandomAugment [1] for data augmentation. The default settings for finetuning on each dataset are shown in Table 1.