Supervised Instance Segmentation

Supervised instance segmentation aims to precisely delineate individual objects within an image, going beyond simple object detection. Current research emphasizes efficient algorithms, particularly those leveraging minimal annotations like bounding boxes or even just a few key points per object, to reduce the high cost of pixel-level labeling. These methods often incorporate techniques such as pseudo-label generation, mutual distillation between semantic and instance information, and active learning strategies to maximize model performance with limited data. This field is crucial for advancing applications requiring fine-grained object understanding, such as autonomous driving and medical image analysis, where fully annotated datasets are often unavailable or prohibitively expensive to create.