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HIRI-ViT: Scaling Vision Transformer with High Resolution Inputs

Core Concepts
HIRI-ViT introduces a new hybrid backbone design tailored for high-resolution inputs, achieving superior performance with comparable computational cost.
The article introduces HIRI-ViT, a five-stage Vision Transformer tailored for high-resolution inputs. HIRI-ViT decomposes CNN operations into two parallel branches to balance performance and computational cost. Experimental results show improved accuracy on ImageNet-1K dataset compared to existing models. The architecture details of HIRI-ViT are provided, showcasing the efficiency in scaling up Vision Transformer with high resolution inputs.
Experiments on ImageNet-1K dataset demonstrate that HIRI-ViT achieves the best Top-1 accuracy of 84.3% with 448×448 inputs under comparable computational cost (∼5.0 GFLOPs).
"HIRI-ViT is built upon the seminal idea of decomposing the typical CNN operations into two parallel CNN branches in a cost-efficient manner." "By enlarging the input resolution from 224×224 to 384×384, a clear performance boost is attained for our HIRI-ViT."

Key Insights Distilled From

by Ting Yao,Yeh... at 03-19-2024

Deeper Inquiries

How does the two-branch design of HIRI-ViT contribute to balancing model capacity and computational cost

HIRI-ViT's two-branch design plays a crucial role in balancing model capacity and computational cost by efficiently scaling Vision Transformer with high-resolution inputs. The high-resolution branch captures coarse-level information over the input, while the low-resolution branch uses more convolution operations to extract high-level semantics. This division of labor allows for maintaining model capacity with high-resolution inputs while reducing computational costs. By decomposing typical CNN operations into two parallel branches, HIRI-ViT achieves a favorable balance between performance and computational overhead tailored for high resolution.

What implications could the EMA distillation strategy have on training efficiency and model performance

The EMA distillation strategy can significantly impact training efficiency and model performance by introducing bidirectional message interaction between teacher and student networks. Unlike traditional knowledge distillation methods, EMA distillation leverages the probability distribution learned from the teacher network to guide the training of the student network. This approach enhances learning by incorporating knowledge from both networks during training, leading to improved convergence speed, stability, and potentially better generalization performance.

How might incorporating high-resolution inputs impact real-world applications beyond image classification tasks

Incorporating high-resolution inputs in real-world applications beyond image classification tasks can have several implications. For object detection tasks, higher resolution inputs can improve localization accuracy and enable better detection of smaller objects or details in images. In instance segmentation applications, increased resolution can enhance segmentation quality by providing finer details for precise object delineation. Similarly, semantic segmentation tasks may benefit from higher resolutions as they allow for more detailed pixel-wise predictions and better understanding of complex scenes or objects within an image.