Summary of Sag-vit: a Scale-aware, High-fidelity Patching Approach with Graph Attention For Vision Transformers, by Shravan Venkatraman et al.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers

by Shravan Venkatraman, Jaskaran Singh Walia, Joe Dhanith P R

First submitted to arxiv on: 14 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The SAG-ViT model proposes a novel approach to image classification by combining the strengths of Vision Transformers (ViTs) and Graph Transformers. The key innovation is the integration of multi-scale feature representations, which is typically achieved through convolutional neural networks (CNNs). The model uses a Scale-Aware Graph Attention ViT architecture that extracts multi-scale feature maps from an EfficientNetV2 backbone, divides them into patches to preserve richer semantic information, and then structures these patches into a graph using spatial and feature similarities. A Graph Attention Network (GAT) refines the node embeddings, which are then processed by a Transformer encoder to capture long-range dependencies and complex interactions. The model is evaluated on benchmark datasets across various domains, demonstrating its effectiveness in advancing image classification tasks.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Imagine you’re trying to recognize objects in pictures. Most computers use a special kind of brain called a neural network to help with this task. One type of neural network is called Vision Transformer (ViT). It’s very good at finding patterns and details in images, but it has trouble understanding things that are connected across the whole image. Another type of neural network is called Graph Transformer. It’s great at looking at relationships between different parts of an image, but it doesn’t do as well with understanding the overall structure of the image. To solve this problem, scientists created a new model called SAG-ViT. This model combines the strengths of ViTs and Graph Transformers by using a special kind of attention to look at both small details and big patterns in images.