VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Table 2: Comparison to the state-of-the-art methods with single model on the VQA dataset. † indicates concurrent works.
['Model', 'test-dev', 'test-std']
Pre-training VL-BERT improves the performance by 1.6%, which validates the importance of pre-training. VL-BERT shares the same input (i.e., question, image, and RoIs), output and experimental protocol with BUTD, a prevalent model specifically designed for the task. Still, VL-BERT surpasses BUTD by over 5% in accuracy. Except for LXMERT, our VL-BERT achieves better performance than the other concurrent works. This is because LXMERT is pre-trained on massive visual question answering data (aggregating almost all the VQA datasets based on COCO and Visual Genome). While our model is only pre-trained on captioning and text- only dataset, where there is still gap with the VQA task.