Deep Self-Supervised Representation Learning for Free-Hand Sketch
Open accessAbstract
In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches. This importantly addresses a common problem faced by the sketch community – that annotated supervisory data are difficult to obtain. This problem is very challenging in which sketches are highly abstract and subject to different drawing styles, making existing solutions tailored for photos unsuitable. Key for the success of our self-supervised learning paradigm lies with our sketch-specific designs: (i) we propose a set of pretext tasks specifically designed for sketches that mimic different drawing styles, and (ii) we further exploit the use of the textual convolution network (TCN) together with the convolutional neural network (CNN) in a dual-branch architecture for sketch feature learning, as means to accommodate the sequential stroke nature of sketches. We demonstrate the superiority of our sketch-specific designs through two sketch-related applications (retrieval and recognition) on a million-scale sketch dataset, and show that the proposed approach outperforms the state-of-the-art unsupervised representation learning methods, and significantly narrows the performance gap between with supervised representation learning.11PyTorch code of this work is available athttps://github.com/zzz1515151/self-supervised_learning_sketch.
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To the authors' knowledge, only a few surveys on video-language pretraining are relevant to multimodal learning.
“To our knowledge, only a few surveys on video-language pretraining (VLP) [57], [58], [59] are relevant to MML.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [58]
Pretext tasks can be combined in practice.
“In practice, pretext tasks can be combined, and some representative cases are summarized in Table 3 of [57], Table 2 of [58].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [58]
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Light-SRNet: a lightweight dual-attention feature fusion network for hand-drawn sketch recognition
Hou, X., Rong, X., & Yu, X. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.1.013005 · Cited by 4 · see citing works
Learning Deep Co-Occurrence Features
Li, G., Li, B., Tan, S., & Qiu, G. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3216905 · Cited by 3 · see citing works
Co-creative Drawing with One-Shot Generative Models
Wieluch, S., & Schwenker, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72914-1_31 · Cited by 3 · see citing works
Capture More Structured Context by Vision Transformers for Free-Hand Sketch Recognition
Guo, W., Liu, S., Yu, Y., & Cai, B. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10393922 · Cited by 2 · see citing works
Deep Learning-Based Recognition System for Hand-Drawn Sketches by Children with Down Syndrome
Sampath, T., Kalpani, A., & Chandrasiri, S. (2024) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac64487.2024.10851187 · Cited by 2 · see citing works
I2DKPCN: an unsupervised deep learning network
Zhao, R., & Shi, F. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03007-9 · Cited by 2 · see citing works
A critical review of sketch collection methods: Remembering how humans really sketch
Dede, E., Kara, Z. B., & Sezgin, T. M. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104603 · Cited by 1 · see citing works
A Simple Convolutional Neural Network for Doodle Classification
Deepkumar, N., & Ankalaki, S. (2022) · IEEE North Karnataka Subsection Flagship International Conference (NKCon) · doi:10.1109/nkcon56289.2022.10126970 · Cited by 1 · see citing works
2021 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 31
(2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3134129
Boosting Fine-Grained Sketch-Based Image Retrieval with Self-Supervised Learning
Zhang, Z., Chen, Y., Zhang, Y., Feng, R., & Zhang, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095145
A Novel Style Takagi-Sugeno-Kang Fuzzy Classifier With Its Fast Training on Style Data
Gu, S., Chung, F.-L., & Wang, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312816
Comparative Analysis of Attention-Based Sketch Recognition
Pande, A., Kukretti, A., & Parihar, A. S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307792
Self-Supervised Learning of Free-Hand Sketches with Bézier Curve Features
Gülez, T., & Sert, M. (2023) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism59092.2023.00030
Category-Level Fine-Grained Sketch-Based 3D Shape RetrievalPreprint
Wang, B., Zhou, Y., & Kung, S. (2025) · Research Square · doi:10.21203/rs.3.rs-10281515/v1
Equipping sketch patches with context-aware positional encoding for graphic sketch representation
Zang, S., & Fang, Z. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104385
Stereo matching via self-supervised pre-training with view synthesis-based pretext task
Xu, L., Zhu, J., Peng, B., Zhang, Z., & Lei, J. (2025) · Optics Express · doi:10.1364/oe.562123
Singh, S., & Raj, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-2872-1_26
Part-Level Semantic Fusion for Sketch-Based 3D Voxel Reconstruction
Wang, F., Pan, Y., Jiang, J., Jiang, D., Zhao, B., & Luo, X. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3675846
- Authors
- Xu, P., Song, Z., Yin, Q., Song, Y.-Z., & Wang, L.
- Affiliations
- Nanyang Technological University (SG)
- Beijing University of Posts and Telecommunications (CN)
- Chinese Academy of Sciences (CN)
- University of Surrey (GB)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2021-04
- Volume
- 31
- Issue
- 4
- Pages
- 1503-1513
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W3036435328
- Cited by
- 53
- References
- 62
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 13 · 2022: 15 · 2021: 10 · 2020: 3
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- hand-drawn sketches, self-supervised representation learning, pretext tasks, dual-branch architecture, sketch retrieval, sketch recognition, unsupervised learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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