Visual Transformers (ViT) are a class of models that apply transformer architectures, originally designed for natural language processing tasks, to computer vision tasks. They have gained significant attention due to their ability to achieve state-of-the-art performance on various image classification benchmarks.
What are Visual Transformers?
Visual Transformers are a type of deep learning model that use transformer architectures for image processing tasks. Unlike traditional convolutional neural networks (CNNs) that process images in a hierarchical manner, ViTs treat images as a sequence of patches and process them in parallel, leveraging the self-attention mechanism of transformers.
How do Visual Transformers work?
ViTs start by dividing an input image into a grid of patches. Each patch is then flattened and linearly transformed into a vector. These vectors are treated as the input sequence for the transformer. Positional embeddings are added to these vectors to retain information about the original location of each patch in the image.
The transformer then processes this sequence of vectors using self-attention and feed-forward layers. The self-attention mechanism allows the model to weigh the importance of each patch in relation to others, enabling it to capture long-range dependencies between patches. The output of the transformer is a sequence of vectors, each representing a patch of the image.
The final vector (corresponding to the ‘CLS’ token in NLP transformers) is used for classification tasks. It is passed through a linear layer followed by a softmax function to produce class probabilities.
Why are Visual Transformers important?
ViTs have several advantages over traditional CNNs. They can model long-range dependencies between pixels, which can be beneficial for tasks that require understanding the global context of an image. They are also more parameter-efficient, as they do not require the extensive hyperparameter tuning that CNNs often do.
Moreover, ViTs can be pre-trained on large-scale image datasets and fine-tuned on specific tasks, similar to how transformers are used in NLP. This allows them to leverage the power of transfer learning, which can significantly improve performance on tasks with limited training data.
Use Cases of Visual Transformers
ViTs have been successfully applied to a wide range of computer vision tasks, including image classification, object detection, and semantic segmentation. They have achieved state-of-the-art performance on several image classification benchmarks, such as ImageNet.
In addition, ViTs have been used in multimodal tasks that involve both images and text, such as image captioning and visual question answering. Their ability to process sequences makes them well-suited for these tasks, as they can handle both the image and text inputs in a unified manner.
Limitations of Visual Transformers
Despite their advantages, ViTs also have some limitations. They require large amounts of data and computational resources for training, which can make them impractical for some applications. They also lack the inductive biases of CNNs, such as translation invariance, which can make them less effective for certain tasks.
However, ongoing research is addressing these limitations, and the future of ViTs in computer vision looks promising. As transformer architectures continue to evolve, we can expect to see further improvements in the performance and efficiency of ViTs.