Selective Scan

Selective scan, a technique employed within state-space models (SSMs), aims to efficiently process visual data by strategically selecting and ordering feature information for improved representation learning. Current research focuses on optimizing selective scan mechanisms within SSM architectures like Mamba, exploring variations such as spatial-channel selective scans and windowed approaches to enhance performance in diverse tasks like image super-resolution, segmentation, and enhancement. These advancements offer a compelling alternative to traditional CNNs and Transformers, particularly in scenarios demanding efficient processing of large datasets or long-range dependencies, with implications for various computer vision applications and potentially other fields.