Pseudo Labeled Image

Pseudo-labeled images leverage unlabeled data to improve the performance of image analysis models, primarily addressing the scarcity of labeled data in various applications. Current research focuses on generating high-quality pseudo-labels using techniques like diffusion models, spectral clustering, and active learning, often integrated with deep learning architectures such as transformers and convolutional neural networks. This approach significantly reduces the annotation burden for tasks like segmentation, object detection, and anomaly detection, impacting fields ranging from medical imaging and remote sensing to 3D scene understanding and disaster response. The resulting improvements in model accuracy and efficiency are particularly valuable in resource-constrained environments.