VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Table 1: Comparison to the state-of-the-art methods with single model on the VCR dataset. † indicates concurrent works.
['Model', 'Q → A', 'Q → A', 'QA → R', 'QA → R', 'Q → AR', 'Q → AR']
Pre-training VL-BERT improves the performance by 1.0% in the final Q → AR task, which validates the effectiveness of pre-training. Compared with R2C, we do not use ad-hoc task-specific modules. Instead, we simply adopt the generic representation of VL-BERT and jointly train the whole model end-to-end. Despite the same input, output and experimental protocol as R2C, VL-BERT outperforms R2C by large margins, indicating the power of our simple cross-modal architecture. Compared with other concurrent works, i.e., ViLBERT, VisualBERT and B2T2, our VL-BERT achieves the state-of-the-art performance.