BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion

arXiv:2401.01642v3 Announce Type: replace
Abstract: Perceiving the complete shape of occluded objects is essential for human and machine intelligence. While the amodal segmentation task is to predict the complete mask of partially occluded objects, it is time-consuming and labor-intensive to annotate the pixel-level ground truth amodal masks. Box-level supervised amodal segmentation addresses this challenge by relying solely on ground truth bounding boxes and instance classes as supervision, thereby alleviating the need for exhaustive pixel-level annotations. Nevertheless, current box-level methodologies encounter …

abstract arxiv blade box cs.cv expansion human human and machine intelligence labor machine machine intelligence masks objects pixel segmentation through truth type