Pixel Classification

Pixel classification, a fundamental task in computer vision, aims to assign a semantic label to each pixel in an image, enabling tasks like semantic segmentation and image super-resolution. Recent research emphasizes improving accuracy and efficiency by incorporating physical constraints, adaptive resource allocation based on pixel-level difficulty, and leveraging structural information within images. These advancements, often employing transformer networks and novel training strategies like contrastive learning and multi-label classification, lead to more robust and accurate pixel-level predictions. The resulting improvements have significant implications for various applications, including medical image analysis, remote sensing, and autonomous driving.