Unsupervised Salient Object Detection

Unsupervised salient object detection (USOD) aims to identify visually prominent objects in images without relying on manually labeled training data, a significant challenge in computer vision. Recent research focuses on developing end-to-end models and leveraging self-supervised learning techniques, often incorporating contrastive learning, graph-based methods (like normalized cut), and refined pseudo-label generation strategies to improve accuracy. These advancements are crucial for reducing the reliance on expensive and time-consuming annotation processes, potentially impacting various applications such as autonomous driving and robotics where large labeled datasets are often unavailable.