Pixel Wise Segmentation

Pixel-wise segmentation aims to assign a class label to each pixel in an image, enabling detailed object delineation. Current research emphasizes improving segmentation accuracy with limited labeled data, exploring techniques like weakly supervised learning using scribbles or imprecise bounding boxes, and employing novel architectures such as U-Net variants and large multi-modal models. These advancements are driving progress in diverse applications, including medical image analysis, urban planning (through 3D model reconstruction from floor plans), and automated visual inspection, where precise segmentation is crucial for accurate analysis and decision-making.