Object Discovery

Object discovery in computer vision focuses on automatically identifying and segmenting individual objects within images and videos without relying on pre-existing object labels. Current research emphasizes unsupervised and weakly-supervised approaches, employing architectures like autoencoders, transformers, and graph neural networks, often incorporating self-supervised learning and contrastive methods to improve object localization and representation. These advancements are crucial for enabling robots to interact with and understand their environments, improving medical image analysis, and advancing various other applications requiring robust scene understanding.