A Spatial Attention Module (SAM) is a type of module used for spatial attention in Convolutional Neural Networks (CNNs). The SAM generates a spatial attention map by utilizing the spatial relationship of different features. This type of attention is different from the channel attention, which focuses on identifying informative channels in the input.
What is Spatial Attention?
Spatial attention is a mechanism that allows CNNs to focus on the most informative parts of the input image. This is e
Spatial-Reduction Attention (SRA):
What is Spatial-Reduction Attention?
Spatial-Reduction Attention (SRA) is a type of multi-head attention used in the Pyramid Vision Transformer architecture. Its purpose is to reduce the scale of the key and value before the attention operation takes place. This means that the computational and memory requirements needed for the attention layer are reduced.
How Does SRA Work?
The SRA in stage i can be formulated as follows:
$$ \text{SRA}(Q, K, V)=\text {
Spatially Separable Self-Attention: A Method to Reduce Complexity in Vision Transformers
As computer vision tasks become more complex and require higher resolution inputs, the computational complexity of vision transformers increases. Spatially Separable Self-Attention, or SSSA, is an attention module used in the Twins-SVT architecture that aims to reduce the computational complexity of vision transformers for dense prediction tasks.
SSSA is composed of locally-grouped self-attention (LSA) and
Talking-Heads Attention: An Introduction
Exploring Multi-Head Attention and Softmax Operation
Human-like understanding and comprehension are the two fundamental concerns of artificial intelligence (AI) and natural language processing (NLP). Communication, comprehension, and reasoning in natural language are the primary objectives of NLP, which is concerned with creating human-like processing systems for textual inputs. In recent years, attention mechanisms have become a dominant trend in NLP
Understanding Triplet Attention
Triplet Attention is a technique used in deep learning to improve the performance of convolutional neural networks, which are used for image recognition, object detection, and many other computer vision applications. It works by breaking down an input image into three parts or branches, each responsible for capturing a different type of information.
The three branches of Triplet Attention are designed to capture cross-dimensional features between the spatial dim