Understanding Neighborhood Attention
Neighborhood Attention is a concept used in Hierarchical Vision Transformers, where each token has its receptive field…
Factorized Dense Synthesized Attention: A Mechanism for Efficient Attention in Neural Networks
Neural networks have shown remarkable performance in many…
Factorized Random Synthesized Attention is an advanced technique used in machine learning architecture, specifically with the Synthesizer model. It is…
Cross-Covariance Attention: A Feature-Based Attention Mechanism
Cross-Covariance Attention, also known as XCA, is an attention mechanism that operates along the…
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.…
In Relation-Aware Global Attention, Global Structural Information is Key
Relation-Aware Global Attention (RGA) is an approach to machine learning that…
Channel-wise Soft Attention is a sophisticated attention mechanism that can significantly improve the performance of computer vision models. It assigns…
Dynamic convolution is a novel operator design that increases the representational power of lightweight CNNs, without increasing their computational cost…
GSoP-Net Overview: Modeling High-Order Statistics and Gathering Global Information
GSoP-Net is a deep neural network architecture that includes a Gsop…