A research team has developed a computer vision technique that can perform dichotomous image segmentation, high-resolution salient object detection, and concealed object detection in the same framework. Their novel bilateral reference framework (BiRefNet) is able to capture tiny-pixel features and holds potential for a wide range of practical computer vision applications.A research team has developed a computer vision technique that can perform dichotomous image segmentation, high-resolution salient object detection, and concealed object detection in the same framework. Their novel bilateral reference framework (BiRefNet) is able to capture tiny-pixel features and holds potential for a wide range of practical computer vision applications.[#item_full_content]

A team of AI researchers and computer scientists at the University of Alberta has found that current artificial networks used with deep-learning systems lose their ability to learn during extended training on new data. In their study, reported in the journal Nature, the group found a way to overcome these problems with plasticity in both supervised and reinforcement learning AI systems, allowing them to continue to learn.A team of AI researchers and computer scientists at the University of Alberta has found that current artificial networks used with deep-learning systems lose their ability to learn during extended training on new data. In their study, reported in the journal Nature, the group found a way to overcome these problems with plasticity in both supervised and reinforcement learning AI systems, allowing them to continue to learn.[#item_full_content]