Lightweight Semantic Segmentation

Lightweight semantic segmentation focuses on developing efficient deep learning models for pixel-wise image classification, prioritizing speed and reduced computational cost without sacrificing significant accuracy. Current research emphasizes novel architectures like efficient transformers and CNN hybrids, often incorporating attention mechanisms and multi-scale feature processing to improve context understanding and detail preservation. These advancements are crucial for deploying semantic segmentation in resource-constrained environments, such as embedded medical devices and mobile applications, enabling real-time analysis of images in various fields. The resulting compact models offer a balance between performance and efficiency, broadening the accessibility and applicability of semantic segmentation technology.