Amodal Instance Segmentation

Amodal instance segmentation (AIS) aims to identify and segment objects in images, including their occluded parts, going beyond simply outlining visible regions. Current research heavily utilizes transformer-based architectures and foundation models like Segment Anything Model (SAM), often incorporating shape priors and occlusion-aware modules to improve accuracy, particularly in challenging scenarios like cluttered scenes and videos. This field is crucial for advancing applications such as autonomous driving, robotic manipulation, and scene understanding, where complete object representation is essential despite occlusions. The development of synthetic datasets and efficient algorithms is a major focus to overcome data limitations and computational costs.