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/AGNN

AGNN

Reported on 9 benchmarks across 3 tasks · 2 papers · 2 SOTA

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

Computer Vision8 results

VideoonYouTube-Objects
J· 2020-01-19
70.8
best: 75.1 (FakeFlow)
SOTA
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
Video Object SegmentationonYouTube-Objects
J· 2020-01-19
70.8
best: 75.1 (FakeFlow)
SOTA
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
VideoonDAVIS 2016 val
F· 2020-01-19
79.1
best: 90.2 (DEVA (DIS))
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
VideoonDAVIS 2016 val
G· 2020-01-19
79.9
best: 88.9 (GSANet)
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
VideoonDAVIS 2016 val
J· 2020-01-19
80.7
best: 88.3 (GSANet)
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
Video Object SegmentationonDAVIS 2016 val
F· 2020-01-19
79.1
best: 90.2 (DEVA (DIS))
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
Video Object SegmentationonDAVIS 2016 val
G· 2020-01-19
79.9
best: 88.9 (GSANet)
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807
Video Object SegmentationonDAVIS 2016 val
J· 2020-01-19
80.7
best: 88.3 (GSANet)
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks arXiv:2001.06807

Graphs1 result

Graph RegressiononLipophilicity
RMSE· 2018-03-10
0.963
best: 0.58 (ProtoS-L2)
Attention-based Graph Neural Network for Semi-supervised Learning arXiv:1803.03735