A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking
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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.
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