mac_qin

Reported on 7 benchmarks across 1 task

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

Natural Language Processing7 results

Visual Question Answering (VQA)onGQA Test2019
Accuracy
52.02
best: 89.3 (human)
Visual Question Answering (VQA)onGQA Test2019
Binary
67.35
best: 91.2 (human)
Visual Question Answering (VQA)onGQA Test2019
Consistency
80.44
best: 98.4 (human)
Visual Question Answering (VQA)onGQA Test2019
Distribution
5.64
best: 93.08 (GlobalPrior)
Visual Question Answering (VQA)onGQA Test2019
Open
38.5
best: 87.4 (human)
Visual Question Answering (VQA)onGQA Test2019
Plausibility
83.94
best: 97.2 (human)
Visual Question Answering (VQA)onGQA Test2019
Validity
95.75
best: 98.9 (human)