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Title: Inter-region affinity distillation for road marking segmentation
Authors: Hou, Yuenan
Ma, Zheng
Liu, Chunxiao
Hui, Tak-Wai
Loy, Chen Change
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Hou, Y., Ma, Z., Liu, C., Hui, T. & Loy, C. C. (2020). Inter-region affinity distillation for road marking segmentation. 2020 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 12486-12495.
Project: 2018-T1-002-056 
metadata.dc.contributor.conference: 2020 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)
Abstract: We study the problem of distilling knowledge from a large deep teacher network to a much smaller student net- work for the task of road marking segmentation. In this work, we explore a novel knowledge distillation (KD) ap- proach that can transfer ‘knowledge’ on scene structure more effectively from a teacher to a student model. Our method is known as Inter-Region Affinity KD (IntRA-KD). It decomposes a given road scene image into different re- gions and represents each region as a node in a graph. An inter-region affinity graph is then formed by establishing pairwise relationships between nodes based on their sim- ilarity in feature distribution. To learn structural knowl- edge from the teacher network, the student is required to match the graph generated by the teacher. The proposed method shows promising results on three large-scale road marking segmentation benchmarks, i.e., ApolloScape, CU- Lane and LLAMAS, by taking various lightweight mod- els as students and ResNet-101 as the teacher. IntRA- KD consistently brings higher performance gains on all lightweight models, compared to previous distillation meth- ods. Our code is available at cardwing/Codes-for-IntRA-KD.
Schools: School of Computer Science and Engineering 
Rights: © 2020 The Author(s). This CVPR 2020 paper is the Open Access version, provided by the Computer Vision Foundation.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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