JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
Implement ViG
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 308
- Forks
- 74
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 2
Description
ref of ViG:
Represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for visual tasks
http://arxiv.org/abs/2206.00272 Vision GNN: An Image is Worth Graph of Nodes
https://github.com/huawei-noah/CV-Backbones/tree/master/vig_pytorch
https://gitee.com/mindspore/models/tree/master/research/cv/ViG
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the Vision GNN paper and comparing the linked Huawei PyTorch and MindSpore ViG implementations. Then inspect GraphNeuralNetworks.jl's existing architecture and integration points to determine the required Julia design. Done means ViG is implemented for the repository's visual graph-learning use case, with appropriate validation against the referenced behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100