A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking
View abstract on PubMed
Summary
This summary is machine-generated.This survey explores efficient methodologies for Vision Transformer (ViT) models, addressing their computational costs. It analyzes compact architectures, pruning, knowledge distillation, and quantization to improve performance in resource-constrained environments.
Area Of Science
- Computer Vision
- Deep Learning
- Artificial Intelligence
Background
- Vision Transformers (ViT) excel at global information extraction via self-attention, surpassing Convolutional Neural Networks.
- ViT performance scales with size, parameters, and operations, leading to high computational and memory demands.
- Quadratic increase in self-attention cost with image resolution challenges real-world deployment due to hardware limitations.
Purpose Of The Study
- To investigate efficient methodologies for Vision Transformer (ViT) architectures.
- To ensure sub-optimal estimation performances despite hardware and environmental restrictions.
- To analyze strategies for making ViTs suitable for real-world applications.
Main Methods
- Analysis of four efficient categories: compact architecture, pruning, knowledge distillation, and quantization.
- Introduction of a new metric, Efficient Error Rate, for comparing models based on inference-time hardware impact.
- Mathematical definition and discussion of state-of-the-art efficient ViT methodologies.
Main Results
- Detailed mathematical definitions of efficiency strategies for Vision Transformers.
- Comprehensive description and discussion of current state-of-the-art efficient methodologies.
- Performance analysis of these methodologies across various application scenarios.
Conclusions
- Efficient methodologies are crucial for deploying Vision Transformers in resource-limited settings.
- The Efficient Error Rate metric provides a standardized way to evaluate model efficiency.
- Further research into open challenges and promising directions can advance efficient ViT development.
Related Concept Videos
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Light is absorbed by the rod and cone...

