Sparse Segmentation

Sparse segmentation focuses on efficiently identifying and isolating relevant features within data, prioritizing computational efficiency without sacrificing accuracy. Current research emphasizes developing lightweight architectures, such as UNet variants incorporating attention mechanisms and spiking neural networks, alongside novel algorithms like those based on ℓ₁ decomposition and iterative unfolding, to achieve this balance. These advancements are particularly impactful in resource-constrained environments, finding applications in medical image analysis (e.g., tumor segmentation, retinal vessel detection), gesture recognition using low-resolution sensors, and text reading order determination from complex layouts.