Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

In this work, we present SEEM, a promotable and interactive model for segmenting
everything everywhere all at once in an image. In SEEM, we propose a novel and versatile …

Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This
appealing ability is vital for recognition and understanding. To enable such capability in AI …

In this work, we present OpenSeeD, a simple Open-vocabulary Segmentation and Detection
framework that learns from different segmentation and detection datasets. To bridge the gap …

Deep learning has achieved remarkable results in many computer vision tasks. Deep neural
networks typically rely on large amounts of training data to avoid overfitting. However …

Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg
uses a text encoder to compute embeddings of descriptive input labels (eg," grass" or" …

Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …