Deep Learning for Free-Hand Sketch: A Survey
By Peng Xu, Timothy M. Hospedales, Qiyue Yin, Yi-Zhe Song, Tao A. Xiang, Liang Wang. 2020
153 citations recorded by the source corpus.
Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
In Computer Vision and Pattern Recognition
- Aneeshan Sain
- Ayan Kumar Bhunia
- Tao A. Xiang
- Yi-Zhe Song
- Subhadeep Koley
- Viswanatha Reddy Gajjala
- Yongxin Yang
- Rohit Kundu
In Human-Computer Interaction
Citations in the corpus
- Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (2022, background): “…41, 46, 34, 53, 6, 2], sketch has also been employed in a variety of vision understanding tasks, including segmentation [22], video synthesis [28], representation learning [60, 3], object localisation [55], image-inpainting [62], 3D shape retrieval [31], 3D shape modelling [71], among others [64].”
- Multimodal Learning With Transformers: A Survey (2022, background): “Both 2D and 3D drawing sketches [78], [163] are a kind of sparse graph if we consider their key points along”