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CoViG enhances Vision Graphs for image classification with reduced computational cost

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CoViG, a novel approach to image classification that enhances Vision Graphs (ViGs) by incorporating graph coarsening. This method integrates a MinCUT pooling step into ViG's Grapher component, simplifying intermediate graphs and reducing computational cost. Evaluations on ImageNet demonstrated that CoViG achieves accuracies comparable to existing ViG models while using approximately 51% fewer FLOPs. AI

IMPACT Introduces a more computationally efficient method for image classification using graph neural networks, potentially accelerating research and application in computer vision.

RANK_REASON The cluster describes a new research paper introducing a novel method for image classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CoViG enhances Vision Graphs for image classification with reduced computational cost

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The cluster describes a new research paper introducing a novel method for image classification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Martial Guidez, Stefan Duffner, Christophe Garcia ·

    CoViG: Coarsened Vision GNN for Image Classification

    arXiv:2610.21321v1 Announce Type: new Abstract: Although Graph Neural Networks are widely used in various domains, they remained mostly unexplored in computer vision. Convolutional Neural Networks and Vision Transformers were leading the field, until the quite recent introduction…

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