Vision Transformers (ViTs) are revolutionizing the field of computer vision by achieving state-of-the-art performance in various tasks, surpassing traditional convolutional neural networks (CNNs). ViTs leverage the self-attention mechanism, originally used in natural language processing, to process images by dividing them into patches and treating them as word embeddings.
Recent research has focused on improving the robustness, efficiency, and scalability of ViTs. For instance, PreLayerNorm has been proposed to address the issue of performance degradation in contrast-enhanced images by ensuring scale-invariant behavior. Auto-scaling frameworks like As-ViT have been developed to automate the design and scaling of ViTs without training, significantly reducing computational costs. Additionally, unified pruning frameworks like UP-ViTs have been introduced to compress ViTs while maintaining their structure and accuracy.
Practical applications of ViTs span across image classification, object detection, and semantic segmentation tasks. For example, PSAQ-ViT V2, a data-free quantization framework, achieves competitive results in these tasks without accessing real-world data, making it a potential solution for applications involving sensitive data. However, challenges remain in adapting ViTs for reinforcement learning tasks, where convolutional-network architectures still generally provide superior performance.
In summary, Vision Transformers are a promising approach to computer vision tasks, offering improved performance and scalability compared to traditional CNNs. Ongoing research aims to address their limitations and further enhance their capabilities, making them more accessible and applicable to a wider range of tasks and industries.
Vision Transformer (ViT)
Vision Transformer (ViT) Further Reading
1.Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding http://arxiv.org/abs/2111.08413v1 Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Dong Gu Lee, Wonseok Jeong, Sang Woo Kim2.Auto-scaling Vision Transformers without Training http://arxiv.org/abs/2202.11921v2 Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou3.Vision Transformer: Vit and its Derivatives http://arxiv.org/abs/2205.11239v2 Zujun Fu4.A Unified Pruning Framework for Vision Transformers http://arxiv.org/abs/2111.15127v1 Hao Yu, Jianxin Wu5.CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction http://arxiv.org/abs/2203.04570v1 Zhuoran Song, Yihong Xu, Zhezhi He, Li Jiang, Naifeng Jing, Xiaoyao Liang6.When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture http://arxiv.org/abs/2210.07540v1 Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang7.Reveal of Vision Transformers Robustness against Adversarial Attacks http://arxiv.org/abs/2106.03734v2 Ahmed Aldahdooh, Wassim Hamidouche, Olivier Deforges8.PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers http://arxiv.org/abs/2209.05687v1 Zhikai Li, Mengjuan Chen, Junrui Xiao, Qingyi Gu9.Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels http://arxiv.org/abs/2204.04905v2 Tianxin Tao, Daniele Reda, Michiel van de Panne10.Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training http://arxiv.org/abs/2112.03552v4 Haofei Zhang, Jiarui Duan, Mengqi Xue, Jie Song, Li Sun, Mingli SongVision Transformer (ViT) Frequently Asked Questions
What is the difference between transformer and ViT?
Transformers are a type of neural network architecture initially designed for natural language processing tasks, such as machine translation and text summarization. They rely on self-attention mechanisms to capture long-range dependencies in the input data. Vision Transformers (ViTs), on the other hand, are an adaptation of the transformer architecture for computer vision tasks, such as image classification and object detection. ViTs process images by dividing them into patches and treating them as word embeddings, allowing the self-attention mechanism to capture spatial relationships between image regions.
What is vision transformer used for?
Vision Transformers (ViTs) are used for various computer vision tasks, including image classification, object detection, and semantic segmentation. They have achieved state-of-the-art performance in these tasks, surpassing traditional convolutional neural networks (CNNs). ViTs are particularly useful in scenarios where capturing long-range dependencies and spatial relationships in images is crucial for accurate predictions.
How do you use a ViT transformer?
To use a Vision Transformer (ViT), follow these steps: 1. Preprocess the input image by resizing and normalizing it. 2. Divide the image into non-overlapping patches of a fixed size. 3. Flatten each patch and linearly embed it into a vector representation. 4. Add positional encodings to the patch embeddings to retain spatial information. 5. Feed the resulting sequence of patch embeddings into a transformer architecture. 6. Train the ViT using a suitable loss function, such as cross-entropy for classification tasks. 7. Fine-tune the model on a specific task or dataset, if necessary. There are pre-trained ViT models and libraries available that can simplify this process, allowing you to focus on fine-tuning and applying the model to your specific problem.
What are the different types of vision transformers?
There are several variants of Vision Transformers (ViTs) that have been proposed to address different challenges and improve performance, robustness, and efficiency. Some notable types include: 1. DeiT (Data-efficient Image Transformers): These ViTs are designed to achieve competitive performance with fewer training samples, making them more data-efficient. 2. As-ViT (Auto-scaling Vision Transformers): This framework automates the design and scaling of ViTs without training, significantly reducing computational costs. 3. UP-ViTs (Unified Pruning Vision Transformers): These ViTs use a unified pruning framework to compress the model while maintaining its structure and accuracy. 4. PSAQ-ViT V2: A data-free quantization framework that achieves competitive results in image classification, object detection, and semantic segmentation tasks without accessing real-world data.
How do Vision Transformers compare to Convolutional Neural Networks?
Vision Transformers (ViTs) have demonstrated superior performance in various computer vision tasks compared to traditional Convolutional Neural Networks (CNNs). ViTs leverage the self-attention mechanism to capture long-range dependencies and spatial relationships in images, which can be advantageous over the local receptive fields used by CNNs. However, CNNs still generally provide better performance in reinforcement learning tasks, and they may be more efficient in terms of computational resources and memory usage for certain problems.
What are the limitations and challenges of Vision Transformers?
While Vision Transformers (ViTs) have shown promising results in various computer vision tasks, they still face some limitations and challenges: 1. Computational complexity: ViTs can be computationally expensive, especially for large-scale problems and high-resolution images. 2. Data requirements: ViTs often require large amounts of labeled data for training, which may not be available for all tasks or domains. 3. Adaptability: Adapting ViTs for reinforcement learning tasks remains a challenge, as convolutional-network architectures still generally provide superior performance in these scenarios. 4. Robustness: ViTs can be sensitive to changes in input data distribution, such as contrast-enhanced images, requiring additional research to improve their robustness. Ongoing research aims to address these limitations and further enhance the capabilities of ViTs, making them more accessible and applicable to a wider range of tasks and industries.
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