Patch Attention

Patch attention mechanisms are being actively researched to improve the efficiency and performance of transformer-based models in computer vision. Current efforts focus on developing novel architectures that reduce the computational complexity of standard self-attention, often through techniques like sparse attention, clustering, and adaptive filtering of image patches, leading to models like ParFormer and ClusTR. These advancements are significant because they enable the application of powerful transformer models to resource-constrained environments and large-scale tasks, improving accuracy and efficiency in various applications such as image classification, object detection, and image restoration.