Lightweight Segmentation

Lightweight segmentation focuses on developing efficient deep learning models for image segmentation tasks, prioritizing reduced computational cost and memory footprint without sacrificing accuracy. Current research emphasizes architectures like U-Net variations, transformers, and neural cellular automata, often incorporating attention mechanisms and knowledge distillation techniques to improve performance while minimizing parameters. This area is crucial for deploying segmentation models on resource-constrained devices like mobile phones and embedded systems, enabling applications in diverse fields such as medical image analysis and real-time interactive segmentation. The resulting models offer significant advantages in terms of speed, power efficiency, and accessibility.