LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Table 2: Test-set results. VQA/GQA results are reported on the ‘test-standard’ splits and NLVR2 results are reported on the unreleased test set (‘Test-U’). The highest method results are in bold. Our LXMERT framework outperforms previous (comparable) state-of-the-art methods on all three datasets w.r.t. all metrics.
['Method', 'VQA Binary', 'VQA Number', 'VQA Other', 'VQA  [BOLD] Accu', 'GQA Binary', 'GQA Open', 'GQA  [BOLD] Accu', 'NLVR2 Cons', 'NLVR2  [BOLD] Accu']
Vqa The SotA result is BAN+Counter in \newcitekim2018bilinear, which achieves the best accuracy among other recent works: MFH Yu et al. et al. Although LXMERT does not explicitly take a counting module as in BAN+Counter, our result on the counting-related questions (‘Number’) is still equal or better.