# Universal Perceptual Grouping

arXiv: https://arxiv.org/abs/1808.02312 · HTML: https://mlsanity.com/paper/1808.02312/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 14 of 14)

- [Differentiable Drawing and Sketching](https://mlsanity.com/paper/2103.16194/) (2021)
- [SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction](https://mlsanity.com/paper/2312.03035/) (2023)
- [SketchXAI: A First Look at Explainability for Human Sketches](https://mlsanity.com/paper/2304.11744/) (2023)
- [One Sketch for All: One-Shot Personalized Sketch Segmentation](https://mlsanity.com/paper/2112.10838/) (2021)
- [Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching](https://mlsanity.com/paper/2602.12280/) (2026)
- [ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives](https://mlsanity.com/paper/2111.11759/) (2021)
- [Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition](https://mlsanity.com/paper/2311.18254/) (2023)
- [Equipping Sketch Patches with Context-Aware Positional Encoding for Graphic Sketch Representation](https://mlsanity.com/paper/2403.17525/) (2024)
- [Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing](https://mlsanity.com/paper/2312.07875/) (2023)
- [SketchRef: a Multi-Task Evaluation Benchmark for Sketch Synthesis](https://mlsanity.com/paper/2408.08623/) (2024)
- [Sketch & Paint: Stroke-by-Stroke Evolution of Visual Artworks](https://mlsanity.com/paper/2502.20119/) (2025)
- [Do Time Constraints Re-Prioritize Attention to Shapes During Visual Photo Inspection?](https://mlsanity.com/paper/2104.06984/) (2021)
- [Multimodal Query-guided Object Localization](https://mlsanity.com/paper/2212.00749/) (2022)
- [Annotation-Free Human Sketch Quality Assessment](https://mlsanity.com/paper/2507.20548/) (2025)

## Abstract (arXiv)

In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping (SPG) dataset to date, consisting of 20,000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model. The model is learned with both generative and discriminative losses. The generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a local grouping loss and a novel global grouping loss to enforce global grouping consistency. We show that the proposed model significantly outperforms the state-of-the-art groupers. Further, we show that our grouper is useful for a number of sketch analysis tasks including sketch synthesis and fine-grained sketch-based image retrieval (FG-SBIR).