Overview of Global Sub-Sampled Attention (GSA)
Global Sub-Sampled Attention, or GSA, is a type of local attention mechanism used in the Twins-SVT architecture that summarizes key information for sub-windows and communicates with other sub-windows. This approach is designed to reduce the computational cost needed for attention mechanisms.
Local Attention Mechanisms
Before diving into GSA, it's important to understand what an attention mechanism is. An attention mechanism is a way for neural n
HyperSA: An Overview of Self-Attention Applied to Hypergraphs
As the field of machine learning continues to grow, researchers need to develop new and more powerful ways to approach problems. One growing area of research is the application of self-attention mechanisms to hypergraphs, which are a powerful way to represent complex relationships between data. This article provides an overview of HyperSA, a novel approach to machine learning that combines the power of self-attention with the flexibi
What is LSH Attention?
LSH Attention, short for Locality Sensitive Hashing Attention, is a method used in the area of machine learning. LSH Attention is a replacement for dot-product attention and is designed to enhance the computation capabilities of modified attention mechanisms. It has proven to be highly efficient in situations where the sequence length is long. To better understand LSH Attention, we must first understand the concept of locality-sensitive hashing. LSH Attention belongs to a
A Computation-Friendly Attention Mechanism: Locally-Grouped Self-Attention
Locally-Grouped Self-Attention (LSA) is a type of attention mechanism used in the Twins-SVT architecture. The purpose of this mechanism is to reduce the computational cost of self-attention in neural networks.
How LSA Works
LSA is designed based on the concept of dividing the feature maps of an input image into smaller sub-windows. The image is divided into M x N sub-windows of equal size, and self-attention is applie
Understanding Location-Based Attention Mechanism
Location-based attention is an advanced artificial intelligence (AI) mechanism that provides a powerful tool to computers and machines that mimics and elaborates human cognition, in order to achieve a similar level of precision and responsiveness. Location-based attention aims to help computers and machines to understand the context and make informed decisions based on the geographical location of certain events, landmarks, or groups of people.1
Location Sensitive Attention: An Overview
Location Sensitive Attention is a mechanism that extends the additive attention mechanism to use cumulative attention weights from previous decoder time steps as an additional feature. This allows the model to move forward consistently through the input, mitigating potential failure modes where some subsequences are repeated or ignored by the decoder.
The attention mechanism is a critical component of sequence-to-sequence models, enabling the model to
Understanding MHMA: The Multi-Head of Mixed Attention
The multi-head of mixed attention (MHMA) is a powerful algorithm that combines both self- and cross-attentions to encourage high-level learning of interactions between entities captured in various attention features. In simpler terms, it is a machine learning model that helps computers understand the relationships between different features of different domains. This is especially useful in tasks involving relationship modeling, such as huma
Multiplicative Attention is a technique used in neural networks to align source and target words. It calculates an alignment score function which is faster and more preferred in practice because it can be implemented efficiently using matrix multiplication. The technique can also be used to determine the correlation between source and target words by using a matrix. The final scores are calculated using a softmax which ensures that the sum of the alignment scores is equal to one.
What is Multi
Understanding Neighborhood Attention
Neighborhood Attention is a concept used in Hierarchical Vision Transformers, where each token has its receptive field restricted to its nearest neighboring pixels. It is a type of self-attention pattern proposed as an alternative to other local attention mechanisms. The idea behind Neighborhood Attention is that a token can only attend to the pixels directly surrounding it, rather than all of the pixels in the image.
This concept is similar to Standalone S
What is Random Synthesized Attention?
Random Synthesized Attention is a type of attention used in machine learning models. It is different from other types of attention because it does not depend on the input tokens. Instead, the attention weights are initialized randomly.
This attention method was introduced with the Synthesizer architecture. Random Synthesized Attention is used to improve the performance of these models by learning a task-specific alignment that works well globally across ma
In Relation-Aware Global Attention, Global Structural Information is Key
Relation-Aware Global Attention (RGA) is an approach to machine learning that emphasizes the importance of global structural information, which is provided by pairwise relations, in generating attention maps. This technique comes in two forms, Spatial RGA (RGA-S) and Channel RGA (RGA-C).
RGA-S and RGA-C
RGA-S reshapes the input feature map X to C x (H x W) and computes the pairwise relation matrix R by using Q and K. R
RAN: A Deep Learning Network with Attention Mechanism
Residual Attention Network (RAN) is a deep convolutional neural network that combines residual connections with an attention mechanism. This network is inspired by the ResNet model that has shown great success in image recognition tasks. By incorporating a bottom-up top-down feedforward structure, RAN is able to model both spatial and cross-channel dependencies that lead to consistent performance improvement.
The Anatomy of RAN
In each at
Scaled Dot-Product Attention: A Revolutionary Attention Mechanism
The concept of attention mechanisms has been around for a long time now. They are used in several applications such as image captioning, language translation, and speech recognition. Attention mechanisms can be thought of as a spotlight that highlights a particular portion of the input, allowing the model to focus on those parts. Recently, the concept of scaled dot-product attention has gained popularity due to its effectiveness
Overview of Self-Calibrated Convolutions
Self-calibrated convolution is a technique used to enlarge the receptive field of a neural network by improving its adaptability. This breakthrough technique was developed by Liu et al. and has shown impressive results in image classification and other visual perception tasks such as keypoint and object detection.
What is a Convolution?
Before delving into self-calibrated convolutions, it is important to understand what a convolution is in the context
Self-supervised Equivariant Attention Mechanism, or SEAM, is an exciting new method for weakly supervised semantic segmentation. It is a type of attention mechanism which applies consistency regularization on Class Activation Maps (CAMs) from different transformed versions of the same image, to provide self-supervision to the network. With the introduction of the Pixel Correlation Module (PCM), SEAM is further able to capture context appearance information for each pixel and use it to revise ori
SortCut Sinkhorn Attention is a type of attention model that uses a truncated input sequence in computations. This variant is an extension of Sparse Sinkhorn Attention that performs a post-sorting truncation of the input sequence. The truncation is based on a hard top-k operation on the input sequence blocks within the computational graph. Most attention models usually assign small weights and re-weight themselves during training. However, SortCut Sinkhorn Attention allows explicitly and dynamic
Introduction:
Attention mechanisms have become very popular in deep learning models because they can learn to focus on important parts of the input. However, the standard attention mechanism can require a lot of memory and computation, which can make it difficult to use in large-scale models. To address this issue, a new attention mechanism called Sparse Sinkhorn Attention has been proposed that is capable of learning sparse attention outputs and reducing the memory complexity of the dot-produc
Overview: What is scSE?
If you've ever used an image recognition program, you know how difficult it can be to recognize objects accurately. scSE is a powerful tool that can help improve the accuracy of image recognition systems. scSE stands for spatial and channel squeeze and excitation blocks, which are modules that help encode both spatial and channel information in feature maps. In essence, the scSE block helps networks pay attention to specific regions of images, and this improves the accur