Large-Scale Adversarial Training for Vision-and-Language Representation Learning
(c) Results on RefCOCOg and Flickr30k Image Retrieval (IR) and Text Retrieval (TR).
['Method', 'RefCOCOg val', 'RefCOCOg test', 'RefCOCOg val [ITALIC] d', 'RefCOCOg test [ITALIC] d', 'Flickr30k IR R@1', 'Flickr30k IR R@5', 'Flickr30k IR R@10', 'Flickr30k TR R@1', 'Flickr30k TR R@5', 'Flickr30k TR R@10']
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.