In the field of machine learning, a Class Attention layer or CA layer is a mechanism that is used in vision transformers to extract information from a set of processed patches. It is similar to a self-attention layer, except that it relies on the attention between the class embedding (initialized at CLS in the first CA) and itself plus the set of frozen patch embeddings.
What is a Vision Transformer?
A Vision Transformer is a type of deep learning model that is designed to process visual data like pictures, videos, and other images. This type of model is based on the concept of self-attention, a mechanism that allows the model to focus selectively on different parts of the image to extract important features more efficiently.
How Does a Class Attention Layer Work?
Consider a network with h heads and p patches, and denoting by d the embedding size, the multi-head class-attention is parameterized with several projection matrices, Wq, Wk, Wv, Wo, and the corresponding biases bq, bk, bv, bo. With this notation, the computation of the CA residual block proceeds as follows. We first augment the patch embeddings (in matrix form) as z=[xclass, xpatches]. We then perform the projections:
Q=Wq xclass+bq
K=Wk z+bk
V=Wv z+bv
The class-attention weights are given by:
A=Softmax(Q . Kโแต / โd / h)
The attention is involved in the weighted sum A x V to produce the residual output vector:
outCA=WoAV+bo
which is in turn added to xclass for subsequent processing.
What are the Benefits of Using a Class Attention Layer?
The Class Attention layer helps to extract important features more efficiently from visual data by focusing selectively on different parts of the image. This can make the model more accurate and effective at tasks like image classification and object detection. It is also more flexible than other types of attention mechanisms, allowing for more customized processing to meet specific needs.
The Class Attention layer is a valuable tool in the field of machine learning, particularly for vision transformers. It allows for more efficient extraction of features from visual data by selectively focusing on different parts of the image. This leads to more accurate and effective models for tasks like image classification and object detection.
