Search Results for author:
Found 3 papers, 2 papers with code
Graph Propagation Transformer for Graph Representation Learning
The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks.
Ranked #2 on
Graph Regression
on PCQM4M-LSC
(Validation MAE metric)
Learning Second Order Local Anomaly for General Face Forgery Detection
We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
