Labeling Image

Image labeling, the process of assigning semantic meaning to pixels or regions within an image, is crucial for training computer vision models but faces challenges due to the cost and time involved in manual annotation. Current research focuses on weakly supervised and semi-supervised approaches, leveraging techniques like class activation maps, pseudo-labeling, and diffusion models to generate or refine labels from limited annotated data, often incorporating image-level labels or textual descriptions. These advancements are significant because they reduce the reliance on extensive manual labeling, enabling the development and application of computer vision systems in resource-constrained scenarios, such as medical image analysis and remote sensing.