Vision GNN: An Image is Worth Graph of Nodes
Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for vi ...
sual tasks. We first split the image to a number of patches which are viewed as nodes, and construct a graph by connecting the nearest neighbors. Based on the graph representation of images, we build our ViG model to transform and exchange information among all the nodes. ViG consists of two basic modules: Grapher module with graph convolution for aggregating and updating graph information, and FFN module with two linear layers for node feature transformation. Both isotropic and pyramid architectures of ViG are built with different model sizes. Extensive experiments on image recognition and object detection tasks demonstrate the superiority of our ViG architecture. We hope this pioneering study of GNN on general visual tasks will provide useful inspiration and experience for future research. The PyTorch code is available at https://github.com/huawei-noah/Efficient-AI-Backbones and the MindSpore code is available at https://gitee.com/mindspore/models.
Results & Benchmarks
No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.
Network architecture plays a key role in the deep learning-based computer vision system.
Implementation Evidence Summary
fengbintu/Neural-Networks-on-Silicon is the closest maintained adjacent implementation (Matches contextual method/domain keyword: architecture). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 2098 GitHub stars.
Reproduction Risks
- Adjacent implementations are not paper-verified
- Recommended repository is adjacent and not paper-verified.
Hardware Notes
Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Evidence disclosure
Evidence graph: 3 refs, 3 links.
Utility signals: depth 70/100, grounding 75/100, status medium.
Implementation Status
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No maintained paper-verified implementation was found; start with the closest related repositories below.
- Compare repo methods against the paper equations/algorithm before trusting metrics.
- Create a minimal baseline implementation from the paper and use adjacent repos as references.
Reproduction readiness
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
No verified implementation available
- · No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.
Framework baselines
- Hugging Face Transformers training guide
Modern transformer training baseline.
- PyTorch nn.Transformer docs
Reference transformer building block implementation.
- TorchVision object detection finetuning tutorial
Baseline setup for object detection workflows.
Closest related implementations
These are not paper-verified. Use them as reference points when no direct implementation is available.
- fengbintu/Neural-Networks-on-SiliconAdjacentConfidence: MediumStars: 2,098
Matches contextual method/domain keyword: architecture
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet.
Continue with targeted Hugging Face searches derived from the paper title and method context:
Models
Tip: start with models, then check datasets/spaces if you need evaluation data or demos.
Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.
Research context
194
Citations
0
References
Tasks
Computer science, Graph, Theoretical computer science, Pattern recognition (psychology), Physical Sciences
Methods
Architecture
Domains
Artificial intelligence, Computer vision, Computer Vision and Pattern Recognition
Evaluation & Human Feedback Data
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXExplore Similar Papers
Jump to Paper2Code search queries derived from this paper's research context.
Related papers
-
Search on Paper2Code
W. HODGES'S VIEWS ON THE CAVERNS AS THE ORIGIN OF ARCHITECTURE : On W. Hodges's "A Dissertation on the Protptypes of Architecture, Hindoo, Moorish, Gothic" (2005) Semantic similarity
-
Search on Paper2Code
Architecture as an Art (1946) Semantic similarity
-
Search on Paper2Code
Making space for degenerate thinking: revaluing architecture with Friedrich Nietzsche (2021) Semantic similarity
-
Search on Paper2Code
The Eventification of Exhibiting Architecture (2020) Semantic similarity
-
Search on Paper2Code
Analysis on Academic Earmark of Contemporary Lingnan Architecture School (2013) Semantic similarity
Need human evaluators for your AI research? Scale annotation with expert AI Trainers.