Multi-Knowledge Aggregation and Transfer for Semantic Segmentation

As a popular deep neural networks (DNN) compression technique, knowledge distillation (KD) has attracted increasing attentions recently. Existing KD methods usually utilize one kind of knowledge in an intermediate layer of DNN for classification tasks to transfer useful information from cumbersome teacher networks to compact student networks. However, this paradigm is not very suitable for semantic segmentation, a comprehensive vision task based on both pixel-level and contextual information, since it cannot provide rich information for distillation. In this paper, we propose a novel multi-knowledge aggregation and transfer (MKAT) framework to comprehensively distill knowledge within an intermediate layer for semantic segmentation. Specifically, the proposed framework consists of three parts: Independent Transformers and Encoders module (ITE), Auxiliary Prediction Branch (APB), and Mutual Label Calibration (MLC) mechanism, which can take advantage of abundant knowledge from intermediate features. To demonstrate the effectiveness of our proposed approach, we conduct extensive experiments on three segmentation datasets: Pascal VOC, Cityscapes, and CamVid, showing that MKAT outperforms the other KD methods.