Graph-Guided Architecture Search for Real-Time Semantic Segmentation

Lin, Peiwen, Sun, Peng, Cheng, Guangliang ORCID: 0000-0001-8686-9513, Xie, Sirui, Li, Xi and Shi, Jianping (2020) Graph-Guided Architecture Search for Real-Time Semantic Segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020-6-13 - 2020-6-19.

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Official URL: http://dx.doi.org/10.1109/cvpr42600.2020.00426

Abstract

Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-Time semantic segmentation networks. Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with a new search space where a lightweight model can be effectively explored through the cell-level diversity and latency oriented constraint. Specifically, to produce the cell-level diversity, the cell-sharing constraint is eliminated through the cell-independent manner. Then a graph convolution network (GCN) is seamlessly integrated as a communication mechanism between cells. Finally, a latency-oriented constraint is endowed into the search process to balance the speed and performance. Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS achieves the new state-of-The-Art trade-off between accuracy and speed. In particular, on Cityscapes dataset, GAS achieves the new best performance of 73.5% mIoU with the speed of 108.4 FPS on Titan Xp.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Bioengineering
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	14 Mar 2023 09:28
Last Modified:	24 Apr 2024 11:30
DOI:	10.1109/cvpr42600.2020.00426
Open Access URL:	https://openaccess.thecvf.com/content_CVPR_2020/pa...
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169011