  Performance of trimap-free image matting methods is limited when trying to
decouple the deterministic and undetermined regions, especially in the scenes
where foregrounds are semantically ambiguous, chromaless, or high
transmittance. In this paper, we propose a novel framework named Privileged
Prior Information Distillation for Image Matting (PPID-IM) that can effectively
transfer privileged prior environment-aware information to improve the
performance of students in solving hard foregrounds. The prior information of
trimap regulates only the teacher model during the training stage, while not
being fed into the student network during actual inference. In order to achieve
effective privileged cross-modality (i.e. trimap and RGB) information
distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module
that reinforces the trimap-free students with more knowledgeable semantic
representations and environment-aware information. We also propose an
Attention-Guided Local Distillation module that efficiently transfers
privileged local attributes from the trimap-based teacher to trimap-free
students for the guidance of local-region optimization. Extensive experiments
demonstrate the effectiveness and superiority of our PPID framework on the task
of image matting. In addition, our trimap-free IndexNet-PPID surpasses the
other competing state-of-the-art methods by a large margin, especially in
scenarios with chromaless, weak texture, or irregular objects.
