What is an XCiT Layer?
An XCiT Layer is a fundamental component of the XCiT (eX- tra large Convolutional Transformer) architecture. This architecture is an adaptation of the Transformer architecture, which is popular in natural language processing (NLP), to the field of computer vision.
The XCiT layer uses cross-covariance attention (XCA) as its primary operation. This is a type of self-attention mechanism that involves comparing different elements within a data set, rather than comparing each element to itself. The XCiT layer consists of three main building blocks:
Block 1: Cross-Covariance Attention (XCA)
The XCA operation is the core operation of the XCiT layer. It involves computing the dot product between a set of queries and a set of keys, followed by a softmax function, to obtain a set of attention scores. These attention scores are then used to weight a set of values, producing a set of attended values. The final output of the XCA operation is a concatenation of the attended values.
In contrast to conventional self-attention, which has a quadratic computational complexity with respect to the number of elements in the data set, the XCA operation has a linear computational complexity. This is achieved by transposing the query-key interaction, reducing the number of dot products required.
Block 2: Local Patch Interaction (LPI) Module
The LPI module is used to encourage information sharing between adjacent patches in an image. It involves computing the dot product between each patch and its neighbors, similar to a convolution operation. The output of the LPI module is then added to the output of the XCA operation, producing a set of attended feature maps.
Block 3: Feed-Forward Network (FFN)
The FFN is a simple neural network that is applied to each attended feature map independently. It consists of two linear layers, separated by a non-linear activation function such as ReLU (rectified linear unit).
The output of the FFN is then added to the input feature maps, using a residual connection. This helps to prevent the loss of information during the transformation process, and enables the network to learn more complex functions.
The XCiT layer is a powerful building block for deep learning models in computer vision. Its use of cross-covariance attention, combined with the LPI module and the FFN, enables it to capture complex patterns in images and produce accurate predictions. As the field of computer vision continues to grow and evolve, the XCiT layer is sure to play an increasingly important role.
