Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/BottomUp

BottomUp

Reported on 7 benchmarks across 1 task · 1 paper · 7 SOTA

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· 2017-07-25
49.74
best: 89.3 (human)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998
Visual Question Answering (VQA)onGQA Test2019
Binary· 2017-07-25
66.64
best: 91.2 (human)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998
Visual Question Answering (VQA)onGQA Test2019
Consistency· 2017-07-25
78.71
best: 98.4 (human)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998
Visual Question Answering (VQA)onGQA Test2019
Distribution· 2017-07-25
5.98
best: 93.08 (GlobalPrior)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998
Visual Question Answering (VQA)onGQA Test2019
Open· 2017-07-25
34.83
best: 87.4 (human)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998
Visual Question Answering (VQA)onGQA Test2019
Plausibility· 2017-07-25
84.57
best: 97.2 (human)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998
Visual Question Answering (VQA)onGQA Test2019
Validity· 2017-07-25
96.18
best: 98.9 (human)
SOTA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering arXiv:1707.07998