Scaling Vision Transformer with High Resolution Inputs: HIRI-ViT

Основные понятия

HIRI-ViT introduces a cost-efficient approach to scale up Vision Transformer with high-resolution inputs, achieving superior performance.

Аннотация

HIRI-ViT is a hybrid backbone that upgrades ViT to handle high-resolution inputs efficiently. It decomposes CNN operations into two parallel branches, reducing computational costs while maintaining model capacity. Experimental results show significant performance improvements over existing models under comparable computational costs.

Статистика

HIRI-ViT achieves the best Top-1 accuracy of 84.3% on ImageNet with 448×448 inputs.
The computational cost of Swin Transformer with 384×384 inputs is significantly heavier than that with 224×224 inputs.
HIRI-ViT leads to significant performance improvements even when enlarging the input resolution to 768×768.

Цитаты

"HIRI-ViT paves a new way to scale up the CNN+ViT hybrid backbone with high resolution inputs."
"Experiments demonstrate the superiority of HIRI-ViT in comparison to state-of-the-art ViT and CNN backbones."

Ключевые выводы из

HIRI-ViT

by Ting Yao,Yeh... в arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11999.pdf

Дополнительные вопросы

How does the two-branch design of HIRI-ViT compare to other approaches in terms of computational efficiency

The two-branch design of HIRI-ViT offers significant advantages in terms of computational efficiency compared to other approaches. By decomposing the typical CNN operations into two parallel branches, HIRI-ViT effectively balances performance and computational cost when scaling up Vision Transformer with high-resolution inputs. The high-resolution branch captures coarse-level information with fewer convolution operations, while the low-resolution branch utilizes more convolutions over downsampled features. This approach reduces the overall computational overhead tailored for high resolution inputs, allowing HIRI-ViT to achieve superior performance without a proportional increase in computational cost.

What implications could the success of HIRI-ViT have for future developments in vision transformer technology

The success of HIRI-ViT holds promising implications for future developments in vision transformer technology. Firstly, it demonstrates a principled way to scale up hybrid backbones with high-resolution inputs efficiently, paving the way for advancements in handling larger input resolutions without compromising on computational efficiency. This could lead to improved performance across various vision tasks that require detailed visual information at higher resolutions. Additionally, the innovative design principles behind HIRI-ViT may inspire further research into optimizing backbone architectures for specific input characteristics and task requirements.

How might the principles behind EMA distillation used in training HIRI-ViT be applied in other machine learning contexts

The principles behind EMA distillation used in training HIRI-ViT can be applied in other machine learning contexts to enhance model training and knowledge transfer processes. EMA distillation enables bidirectional message interaction between teacher and student networks by leveraging probability distributions learned from the teacher network during training. This approach can improve model generalization, stability, and convergence by incorporating additional guidance from a pre-trained network without relying on large-scale external models or datasets. Such techniques could be beneficial for knowledge distillation, transfer learning, and ensemble methods across various machine learning applications where model optimization or fine-tuning is required based on existing knowledge sources.