Large-Scale Adversarial Training for Vision-and-Language Representation Learning
(b) Results on RefCOCO+ and RefCOCO. The superscript d denotes evaluation using detected proposals.
['Method', 'RefCOCO+ val', 'RefCOCO+ testA', 'RefCOCO+ testB', 'RefCOCO+ val [ITALIC] d', 'RefCOCO+ testA [ITALIC] d', 'RefCOCO+ testB [ITALIC] d', 'RefCOCO val', 'RefCOCO testA', 'RefCOCO testB', 'RefCOCO val [ITALIC] d', 'RefCOCO testA [ITALIC] d', 'RefCOCO testB [ITALIC] d']
Downstream Task Evaluation Compared with existing pre-trained V+L models, our Villa method achieves new state of the art across all the benchmarks. Specifically, Villa-base model outperforms UNITER-base by +0.76 on VQA, +2.4 on VCR for Q→AR, +1.45 on NLVR2, +0.75 on SNLI-VE, +2.22/+0.70 on Flickr30k for Image/Text Retrieval (R@1), and +0.99 on average for the three RE datasets.