Paper Info
Title
Sketchgan: Joint Sketch Completion And Recognition With Generative Adversarial Network
Abstract
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.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00598
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
ISSN
Computer vision,Generative adversarial network,Video retrieval,Computer science,Sketch recognition,Artificial intelligence,Boosting (machine learning),Sketch
Conference
1063-6919
Citations 
PageRank 
References 
2
0.36
0
Authors
6
Name
Order
Citations
PageRank
Fang Liu121.04
Xiaoming Deng2687.59
Yu-Kun Lai3102580.48
Yong-Jin Liu483772.83
Cuixia Ma520.70
Hongan Wang664279.77