Separable Self Attention

Separable self-attention mechanisms aim to improve the efficiency of self-attention in transformer networks, particularly for resource-constrained applications like mobile vision and lightweight image processing. Current research focuses on developing architectures like SepViT and modifications to existing models (e.g., MobileViT) that leverage depthwise separable convolutions or other strategies to reduce the computational complexity of self-attention from quadratic to linear time. This leads to significant speed improvements and reduced latency while maintaining or even improving performance on various vision tasks, including image classification, object detection, and image super-resolution, making deep learning models more practical for deployment on mobile and embedded devices.