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Poster
Recognizing Vector Graphics without Rasterization
XINYANG JIANG · LU LIU · Caihua Shan · Yifei Shen · Xuanyi Dong · Dongsheng Li

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @
in Poster Session 3 »

In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be scaled up or down into any resolution without aliasing or information loss, due to the analytic representation of the primitives in the document. Furthermore, vector graphics are able to give extra structural information on how low-level elements group together to form high level shapes or structures. These merits of graphic vectors have not been fully leveraged in existing methods. To explore this data format, we target on the fundamental recognition tasks: object localization and classification. We propose an efficient CNN-free pipeline that does not render the graphic into pixels (i.e. rasterization), and takes textual document of the vector graphics as input, called YOLaT (You Only Look at Text). YOLaT builds multi-graphs to model the structural and spatial information in vector graphics, and a dual-stream graph neural network is proposed to detect objects from the graph. Our experiments show that by directly operating on vector graphics, YOLaT outperforms raster-graphic based object detection baselines in terms of both average precision and efficiency. Code is available at https://github.com/microsoft/YOLaT-VectorGraphicsRecognition.

Keywords: Deep Learning · Machine Learning · Vision · Graph Learning

Author Information

XINYANG JIANG (Microsoft Research)
LU LIU (University of Technology Sydney)

Lu Liu is a 3-rd year Ph.D. student from University of Technology Sydney. Her research interests lie in Machine Learning, Meta-learning and Low-shot learning.

Caihua Shan (Microsoft)
Yifei Shen (HKUST)
Xuanyi Dong (University of Technology Sydney)

Xuanyi Dong is 3-rd year Ph.D. student of Centre for Artificial Intelligence at University of Technology Sydney. His research topic is automated deep learning, especially neural architecture search and its application to computer vision. He has published almost 20 papers on top-tiered conferences and journals including CVPR, ICCV, NuerIPS, T-PAMI. He was elected as one of the 2019 Google Ph.D. Fellows.

Dongsheng Li (IBM Research - China)

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