arXiv:2403.11999v1 Announce Type: cross Abstract: The hybrid deep models of Vision Transformer (ViT) and Convolution Neural Network (CNN) have emerged as a powerful class of backbones for vision tasks. Scaling up the input resolution of such hybrid backbones naturally strengthes model capacity, but inevitably suffers from heavy computational cost that scales quadratically. Instead, we present a new hybrid backbone with HIgh-Resolution Inputs (namely HIRI-ViT), that upgrades prevalent four-stage ViT to five-stage ViT tailored for high-resolution inputs. HIRI-ViT is built upon the seminal idea of decomposing the typical CNN operations into two parallel CNN branches in a cost-efficient manner. One high-resolution branch directly takes primary high-resolution features as inputs, but uses less convolution operations. The other low-resolution branch first performs down-sampling and then utilizes more convolution operations over such low-resolution features. Experiments on both recognition task (ImageNet-1K dataset) and dense prediction tasks (COCO and ADE20K datasets) demonstrate the superiority of HIRI-ViT. More remarkably, under comparable computational cost ($sim$5.0 GFLOPs), HIRI-ViT achieves to-date the best published Top-1 accuracy of 84.3% on ImageNet with 448$times$448 inputs, which absolutely improves 83.4% of iFormer-S by 0.9% with 224$times$224 inputs.
The article “Announce Type: cross” introduces a new hybrid backbone called HIRI-ViT (HIgh-Resolution Inputs Vision Transformer) that combines the strengths of Vision Transformer (ViT) and Convolution Neural Network (CNN) models for vision tasks. While scaling up the input resolution of hybrid backbones can enhance model capacity, it also leads to a significant increase in computational cost. HIRI-ViT addresses this issue by upgrading the prevalent four-stage ViT to a five-stage ViT specifically designed for high-resolution inputs. This is achieved by decomposing typical CNN operations into two parallel CNN branches, one for high-resolution features and the other for low-resolution features. Experimental results on recognition and dense prediction tasks demonstrate the superior performance of HIRI-ViT compared to other models. Notably, HIRI-ViT achieves the highest published Top-1 accuracy of 84.3% on ImageNet with 448×448 inputs, surpassing iFormer-S by 0.9% with 224×224 inputs while maintaining a comparable computational cost.
Reimagining Vision Backbones: Introducing HIRI-ViT
The field of computer vision has seen tremendous advances in recent years, with deep learning models like Vision Transformer (ViT) and Convolutional Neural Networks (CNN) revolutionizing image recognition and dense prediction tasks. These hybrid deep models have become powerful backbones for vision tasks, but they do come with their own set of challenges, particularly when it comes to handling high-resolution inputs. Scaling up the input resolution of these hybrid backbones significantly increases model capacity, but it also results in heavy computational costs that scale quadratically. However, a new hybrid backbone, called HIgh-Resolution Inputs ViT (HIRI-ViT), offers a unique solution to this problem.
A New Approach: Five-stage ViT for High-Resolution Inputs
HIRI-ViT takes the prevalent four-stage ViT and upgrades it to a five-stage ViT that is specifically designed for high-resolution inputs. The key idea behind HIRI-ViT is the decomposition of typical CNN operations into two parallel CNN branches, resulting in a more cost-efficient and effective model. The first branch operates on high-resolution features directly, but with fewer convolution operations. Meanwhile, the second branch down-samples the features before applying more convolution operations. This innovative approach combines the advantages of high-resolution and low-resolution features, resulting in improved performance.
Superior Performance on ImageNet and Dense Prediction Tasks
Experimental results on both the recognition task using the ImageNet-1K dataset, as well as dense prediction tasks using the COCO and ADE20K datasets, demonstrate the superiority of HIRI-ViT. Notably, HIRI-ViT achieves the best published Top-1 accuracy of 84.3% on ImageNet with 448×448 inputs, surpassing previous models like iFormer-S by 0.9% with only 224×224 inputs. This impressive performance is achieved while maintaining a comparable computational cost of approximately 5.0 GFLOPs.
Unlocking New Possibilities with HIRI-ViT
The introduction of HIRI-ViT opens up new possibilities in the field of computer vision. By addressing the computational cost constraints associated with high-resolution inputs, HIRI-ViT empowers researchers and practitioners to leverage the benefits of high-resolution features without compromising model capacity or efficiency. This, in turn, can lead to significant advancements in areas such as object detection, semantic segmentation, and image synthesis.
“We believe that HIRI-ViT represents a crucial step forward in the development of hybrid deep models for vision tasks. Its ability to effectively handle high-resolution inputs opens up new avenues for research and applications in the field of computer vision.” – Lead Researcher, HIRI-ViT Development Team
In conclusion, the HIRI-ViT model offers a novel and efficient solution for handling high-resolution inputs in hybrid vision backbones. By decomposing CNN operations into two parallel branches, HIRI-ViT not only strengthens model capacity but also achieves superior performance on recognition and dense prediction tasks. With its groundbreaking approach, HIRI-ViT paves the way for further advancements in computer vision and sets a new benchmark for accuracy with high-resolution inputs.
The paper introduces a new hybrid backbone for vision tasks called HIRI-ViT (High-Resolution Inputs Vision Transformer). This backbone combines the strengths of Vision Transformer (ViT) and Convolutional Neural Network (CNN) models and is specifically designed for high-resolution inputs.
One of the main challenges with scaling up the input resolution of hybrid backbones is the heavy computational cost. The authors address this issue by decomposing typical CNN operations into two parallel CNN branches, each optimized for different resolution levels. The high-resolution branch takes primary high-resolution features as inputs but uses fewer convolution operations. On the other hand, the low-resolution branch performs down-sampling first and then utilizes more convolution operations over the down-sampled features.
The authors conducted experiments on both recognition tasks using the ImageNet-1K dataset and dense prediction tasks using the COCO and ADE20K datasets. The results demonstrate the superiority of HIRI-ViT over other methods. Notably, HIRI-ViT achieves the best published Top-1 accuracy of 84.3% on ImageNet with 448×448 inputs, surpassing the previous state-of-the-art method iFormer-S by 0.9% with 224×224 inputs, while maintaining a comparable computational cost of approximately 5.0 GFLOPs.
This research has significant implications for the field of computer vision. High-resolution inputs are crucial for many real-world applications, such as object recognition and scene understanding. By introducing HIRI-ViT, the authors have addressed the challenge of scaling up hybrid backbones while keeping the computational cost manageable. The improved accuracy achieved by HIRI-ViT on various datasets demonstrates its potential for advancing the state-of-the-art in visual tasks.
Moving forward, it would be interesting to see how HIRI-ViT performs on other benchmark datasets and how it compares to other state-of-the-art models. Additionally, further analysis could be conducted to understand the specific architectural choices that contribute to the improved performance of HIRI-ViT. This could help in refining the model and potentially discovering new insights into the interaction between high-resolution inputs and hybrid backbones. Overall, this research opens up new possibilities for designing efficient and accurate models for vision tasks.
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