Both accuracy and efficiency are of significant importance to the task of
semantic segmentation. Existing deep FCNs suffer from heavy computations due to
a series of high-resolution feature maps for preserving the detailed knowledge
in dense estimation. Although reducing the feature map resolution (i.e.,
applying a large overall stride) via subsampling operations (e.g., pooling and
convolution striding) can instantly increase the efficiency, it dramatically
decreases the estimation accuracy. To tackle this dilemma, we propose a
knowledge distillation method tailored for semantic segmentation to improve the
performance of the compact FCNs with large overall stride. To handle the
inconsistency between the features of the student and teacher network, we
optimize the feature similarity in a transferred latent domain formulated by
utilizing a pre-trained autoencoder. Moreover, an affinity distillation module
is proposed to capture the long-range dependency by calculating the non-local
interactions across the whole image. To validate the effectiveness of our
proposed method, extensive experiments have been conducted on three popular
benchmarks: Pascal VOC, Cityscapes and Pascal Context. Built upon a highly
competitive baseline, our proposed method can improve the performance of a
student network by 2.5\% (mIOU boosts from 70.2 to 72.7 on the cityscapes test
set) and can train a better compact model with only 8\% float operations
(FLOPS) of a model that achieves comparable performances.

针对语义分割中的计算效率和精度问题，本文提出一种知识蒸馏方法，利用预训练自编码器在转移潜空间内优化特征相似度，同时引入亲和力蒸馏模块以计算整个图像的远程依赖，将经典的深度 FCN 用于大步长下进行语义分割，实验证明，所提出的方法不仅可以提高小型 FCN 的精度，还可以使用较少的 FLOPS 训练出优秀的小型模型。