Entity Level

Entity-level processing focuses on identifying and understanding individual entities within data, whether text or images, aiming to improve information extraction and representation. Current research emphasizes weakly-supervised and unsupervised methods, leveraging large language models and advanced techniques like entity masking and multi-modal feature fusion to improve accuracy and efficiency, particularly in open-vocabulary segmentation and relation extraction. This work is significant for advancing natural language processing and computer vision, enabling more robust and scalable applications in diverse fields like biomedical research, crisis response, and image understanding.