The topic of TNT is an innovative approach to computer vision technology that utilizes a self-attention-based neural network called Transformer to process both patch-level and pixel-level representations of images. This novel Transformer-iN-Transformer (TNT) model uses an outer transformer block to process patch embeddings and an inner transformer block to extract local features from pixel embeddings, thereby allowing for a more comprehensive view of the image features. Ultimately, the TNT model
Overview of Twins-PCPVT
Twins-PCPVT is a type of vision transformer that combines global attention with conditional position encodings to improve accuracy in image classification and other visual tasks. This transformer is an advancement from the Pyramid Vision Transformer (PVT), as it uses conditional position encodings instead of absolute position encodings.
Understanding Vision Transformers
Vision transformers are a type of artificial neural network that are used for image recognition and
Overview of Twins-SVT: A Vision Transformer
Twins-SVT is an emerging technology in the field of computer vision that uses a spatially separable attention mechanism to analyze visual data. This technology has been designed to help handle complex visual inputs and enable machines to recognize patterns and classify images with accuracy.
The term "Twins-SVT" refers to a specific type of vision transformer that is made up of two attention operations: locally-grouped self-attention (LSA) for handlin
Overview of Video-Audio-Text Transformer (VATT)
Video-Audio-Text Transformer, also known as VATT, is a framework for learning multimodal representations from unlabeled data. VATT is unique because it uses convolution-free Transformer architectures to extract multidimensional representations that are rich enough to benefit a variety of downstream tasks. This means that VATT takes raw signals, such as video, audio, and text, as inputs and creates representations that can be used for many differen
Overview of Visformer
Visformer is an advanced architecture utilized in the field of computer vision. It is a combination of two popular structures, the Transformer and Convolutional Neural Network (CNN) architectures. This article explains what Visformer is and how it works, discussing the essential features that make it a groundbreaking technology used in computer vision applications.
Basic Components of Visformer
Visformer architected with Transformer-based features specially designed for
Introduction to Vision Transformer
The Vision Transformer, also known as ViT, is a model used for image classification that utilizes a Transformer-like architecture over patches of an image. This approach splits the image into fixed-size patches, and each patch is linearly embedded, added with position embeddings, and then fed into a standard Transformer encoder. To perform classification, an extra learnable "classification token" is added to the sequence.
What is a Transformer?
A Transforme
Introduction to XCiT
Cross-Covariance Image Transformers, or XCiT, is an innovative computer vision technology that combines the accuracy of transformers with the scalability of convolutional architectures. This technique enables flexible modeling of image data beyond the local interactions of convolutions, making it ideal for high-resolution images and long sequences.
What is a Transformer?
In deep learning, transformers are a class of neural networks that excel at processing sequential dat