GvT: A Graph-based Vision Transformer with Talking-Heads Attention for Small Dataset Training

Core Concepts

The proposed Graph-based Vision Transformer (GvT) utilizes graph convolutional projection and talking-heads attention to effectively train on small datasets, outperforming convolutional neural networks and other vision transformer variants.

Abstract

The paper introduces a new architecture for vision transformers called Graph-based Vision Transformer (GvT) that can train from scratch on small datasets and achieve state-of-the-art performance.
Key highlights:

GvT treats the image as graph data and uses graph convolutional projection to learn dependencies among tokens, leveraging inductive bias to attend to local features in early layers.
To address the low-rank bottleneck in attention heads, GvT employs talking-heads attention by performing sparse selection on the attention tensor to eliminate redundancy and enable interaction among filtered attention scores.
GvT also applies graph-pooling between intermediate blocks to reduce the number of tokens and aggregate semantic information more effectively.
Extensive experiments on small vision datasets like ClipArt, CIFAR-100, Oxford-IIIT Pet, Sketch, Chest X-ray, and COVID-CT demonstrate that GvT outperforms convolutional neural networks and other vision transformer variants trained from scratch.
Ablation studies validate the effectiveness of the proposed graph convolutional projection and talking-heads attention in GvT.

Stats

The ClipArt dataset contains 33,525 training images and 14,604 testing images, divided into 345 different object categories, with an average of 97 images per category.
The CIFAR-100 dataset contains 60,000 32x32 color images in 100 classes, with 500 training and 100 testing images per class.
The Oxford-IIIT Pet dataset contains around 100 training images for each of the 37 pet categories.
The Sketch-Subset dataset contains 1,997 training images and 866 testing images across 16 categories.
The Chest X-ray dataset contains 1,600 training images and 2,000 testing images, with 1,800 images in each of the normal and abnormal categories.
The COVID-CT dataset contains 280 training images and 186 testing images, with 349 positive and 397 negative examples.

Quotes

"When training from scratch on small datasets, there is still a significant performance gap between ViTs and Convolutional Neural Networks (CNNs), which is attributed to the lack of inductive bias."
"To address this issue, we propose a Graph-based Vision Transformer (GvT) that utilizes graph convolutional projection and graph-pooling."
"To overcome the low-rank bottleneck in attention heads, we employ talking-heads technology based on bilinear pooled features and sparse selection of attention tensors."

Key Insights Distilled From

GvT

by Dongjing Sha... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04924.pdf

Deeper Inquiries

How can the proposed GvT architecture be extended to other computer vision tasks beyond image classification, such as object detection or semantic segmentation

The GvT architecture can be extended to other computer vision tasks beyond image classification by adapting its components to suit the requirements of tasks like object detection or semantic segmentation. For object detection, the graph convolutional projection and talking-heads mechanism in GvT can be utilized to capture spatial relationships between objects in an image. By incorporating region proposal networks and bounding box regression heads, GvT can be modified to detect and localize objects within an image. Additionally, for semantic segmentation, the graph-based approach in GvT can be leveraged to understand pixel-level relationships and dependencies. By incorporating skip connections and decoder modules, GvT can segment images into different classes or regions based on learned features and relationships.

What are the potential limitations or drawbacks of the talking-heads approach used in GvT, and how could they be addressed in future work

While the talking-heads approach used in GvT has shown promising results in enhancing the expressive power of attention heads, there are potential limitations and drawbacks that need to be addressed. One limitation is the increased computational complexity introduced by the additional linear projections and sparse selection operations. This can lead to longer training times and higher resource requirements. To address this, future work could focus on optimizing the implementation of talking-heads to reduce computational overhead without compromising performance. Another drawback is the potential for information loss during the sparse selection process, which may impact the model's ability to capture fine-grained details. Future research could explore alternative methods for selecting and combining attention heads to mitigate this issue and improve the overall performance of the model.

Given the success of GvT on small datasets, how could the insights from this work be applied to improve the performance of vision transformers on large-scale datasets as well

The insights from the success of GvT on small datasets can be applied to improve the performance of vision transformers on large-scale datasets by focusing on several key areas. Firstly, the inductive bias introduced by the graph convolutional projection in GvT can be leveraged to capture long-range dependencies and spatial relationships in larger datasets. By scaling up the model architecture and incorporating more complex graph structures, vision transformers can better handle the increased data volume and diversity present in large-scale datasets. Additionally, the lessons learned from addressing the low-rank bottleneck in attention heads using talking-heads technology can be applied to enhance the scalability and efficiency of vision transformers on larger datasets. By optimizing the model architecture and training strategies based on the insights gained from GvT, vision transformers can achieve superior performance on large-scale datasets without the need for extensive pre-training.