Class relationship‐based knowledge distillation for efficient human parsing

In computer vision, human parsing is challenging due to its demand for accurate region location and semantic partitioning. This dense prediction task needs powerful computation high-precision models. To enable real-time on resource-limited devices, the authors introduced a lightweight model using ResNet18 as core network . The simplified pyramid module, improving context clarity reducing complexity. integrated spatial attention fusion strategy counter precision loss in light-weighting process. Traditional models, despite their segmentation precision, are limited by computational complexity extensive parameters. implemented knowledge distillation (KD) techniques enhance authors’ network's accuracy. methods can fail learn useful with significant differences. Hence, used novel approach based inter-class intra-class relations outcomes, noticeably experiments Look into Person (LIP) dataset show that significantly reduces parameters while maintaining enhancing inference speed.