mlsanityData to 7 Oct 2026

Semi-supervised reference-based sketch extraction using a contrastive learning framework

arXiv 2407.14026 ↗First posted 19 Jul 2024cs.CVcited by 5

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Abstract (arXiv)

Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 48 of its 95 references that are in the map. First posted on arXiv 19 Jul 2024.

Builds on

48 of 95 references.

References · 48

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36 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attentioncited by 3
  2. 2025StrokeFusion: Vector Sketch Generation via Joint Stroke-UDF Encoding and Latent Sequence Diffusioncited by 2
  3. 2025Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generationcited by 1
  4. 2026Workflow-Aware Structured Layer Decomposition for Illustration Productioncited by 1
  5. 2025Text to Sketch Generation with Multi-Stylescited by 0