Search Results for author:
Found 2 papers, 2 papers with code
Improving existing segmentators performance with zero-shot segmentators
We release with this paper the open-source implementation of our method.
Ranked #4 on
Camouflaged Object Segmentation
on CAMO
(MAE metric, using extra
training data)
Substructure Aware Graph Neural Networks
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
Ranked #7 on
Graph Regression
on ZINC-500k
