VL-BERTBASE

Reported on 4 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing4 results

Visual Question Answering (VQA)onVCR (Q-AR) dev
Accuracy· 2019-08-22
55.2
best: 58.9 (VL-BERTLARGE)
VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530
Visual Question Answering (VQA)onVCR (Q-A) dev
Accuracy· 2019-08-22
73.8
best: 75.5 (VL-BERTLARGE)
VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530
Visual Question Answering (VQA)onVCR (QA-R) dev
Accuracy· 2019-08-22
74.4
best: 77.9 (VL-BERTLARGE)
VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530
Visual Question Answering (VQA)onVQA v2 test-dev
Accuracy· 2019-08-22
71.16
best: 84.3 (PaLI)
VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530