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SketchGAN: Joint sketch completion and recognition with generative adversarial network

Liu, Fang, Deng, Xiaoming, Lai, Yukun ORCID:, Liu, Yong-Jin, Ma, Cuixia and Wang, Hongan 2019. SketchGAN: Joint sketch completion and recognition with generative adversarial network. Presented at: CVPR, Long Beach, California, USA, 16-20 June 2019.

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Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input; however, hand-drawn sketches in common application scenarios are often incomplete, which makes sketch recognition a challenging problem. In this paper, we propose SketchGAN, a new generative adversarial network (GAN) based approach that jointly completes and recognizes a sketch, boosting the performance of both tasks. Specifically, we use a cascade Encode-Decoder network to complete the input sketch in an iterative manner, and employ an auxiliary sketch recognition task to recognize the completed sketch. Experiments on the Sketchy database benchmark demonstrate that our joint learning approach achieves competitive sketch completion and recognition performance compared with the state-of-the-art methods. Further experiments using several sketch-based applications also validate the performance of our method.

Item Type: Conference or Workshop Item (Paper)
Status: Unpublished
Schools: Computer Science & Informatics
Funders: Royal Society
Date of First Compliant Deposit: 5 April 2019
Last Modified: 25 Oct 2022 14:05

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