Dynamic graph message passing networks
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although CNNs have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels. A fully-connected graph is b...
Main Authors: | Zhang, L, Xu, D, Arnab, A, Torr, PHS |
---|---|
Format: | Conference item |
Language: | English |
Published: |
IEEE
2020
|
Similar Items
-
Dynamic graph message passing networks for visual recognition
by: Zhang, L, et al.
Published: (2022) -
Graph inductive biases in transformers without message passing
by: Ma, L, et al.
Published: (2023) -
Graph inductive biases in transformers without message passing
by: Ma, L, et al.
Published: (2023) -
Polarized message-passing in graph neural networks
by: He, Tiantian, et al.
Published: (2024) -
A graph neural network with negative message passing and uniformity maximization for graph coloring
by: Xiangyu Wang, et al.
Published: (2024-03-01)