VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Table 3: Comparison to the state-of-the-art methods with single model on the RefCOCO+ dataset. † indicates concurrent work.
['Model', 'Ground-truth Regions', 'Ground-truth Regions', 'Ground-truth Regions', 'Detected Regions', 'Detected Regions', 'Detected Regions']
Pre-trained VL-BERT significantly improves the performance. Compared with MAttNet, VL-BERT is much simpler without task-specific architecture designs, yet much better. VL-BERT achieves comparable performance with the concurrent work of ViLBERT.