# ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives

arXiv: https://arxiv.org/abs/2111.11759 · HTML: https://mlsanity.com/paper/2111.11759/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Tagger: Deep Unsupervised Perceptual Grouping](https://mlsanity.com/paper/1606.06724/) (2016)
- [StructureNet: Hierarchical Graph Networks for 3D Shape Generation](https://mlsanity.com/paper/1908.00575/) (2019)
- [PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation](https://mlsanity.com/paper/1903.00709/) (2019)
- [GRAINS: Generative Recursive Autoencoders for INdoor Scenes](https://mlsanity.com/paper/1807.09193/) (2018)
- [Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories](https://mlsanity.com/paper/2002.06478/) (2020)
- [GRASS: Generative Recursive Autoencoders for Shape Structures](https://mlsanity.com/paper/1705.02090/) (2017)
- [Universal Perceptual Grouping](https://mlsanity.com/paper/1808.02312/) (2018)
- [Learning to Group and Label Fine-Grained Shape Components](https://mlsanity.com/paper/1809.05050/) (2018)
- [SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks](https://mlsanity.com/paper/2003.00678/) (2020)

## Built on by (most cited first, 2 of 2)

- [Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure](https://mlsanity.com/paper/2512.14336/) (2025)
- [PolarSym: Polar Geometry-aware Attention for CAD Floorplan Parsing](https://mlsanity.com/paper/2608.11793/) (2026)

## Abstract (arXiv)

Selection functionality is as fundamental to vector graphics as it is for raster data. But vector selection is quite different: instead of pixel-level labeling, we make a binary decision to include or exclude each vector primitive. In the absence of intelligible metadata, this becomes a perceptual grouping problem. These have previously relied on heuristics derived from empirical principles like Gestalt Theory, but since these are ill-defined and subjective, they often result in ambiguity. Here we take a data-centric approach to the problem. By exploiting the recursive nature of perceptual grouping, we interpret the task as constructing a hierarchy over the primitives of a vector graphic, which is amenable to learning with recursive neural networks with few human annotations. We verify this by building a dataset of these hierarchies on which we train a hierarchical grouping network. We then demonstrate how this can underpin a prototype selection tool.