# A Two-stage Framework for Compound Figure Separation

arXiv: https://arxiv.org/abs/2101.09903 · HTML: https://mlsanity.com/paper/2101.09903/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [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)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks](https://mlsanity.com/paper/1703.05105/) (2017)
- [Automatic Separation of Compound Figures in Scientific Articles](https://mlsanity.com/paper/1606.01021/) (2016)

## Built on by (most cited first, 8 of 8)

- [Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery](https://mlsanity.com/paper/2111.01905/) (2021)
- [Compound Figure Separation of Biomedical Images with Side Loss](https://mlsanity.com/paper/2107.08650/) (2021)
- [Plot2Spectra: an Automatic Spectra Extraction Tool](https://mlsanity.com/paper/2107.02827/) (2021)
- [Mystique: Deconstructing SVG Charts for Layout Reuse](https://mlsanity.com/paper/2307.13567/) (2023)
- [Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning](https://mlsanity.com/paper/2208.14357/) (2022)
- [Integrating curation into scientific publishing to train AI models](https://mlsanity.com/paper/2310.20440/) (2023)
- [FigEx2: Visual-Conditioned Panel Detection and Captioning for Scientific Compound Figures](https://mlsanity.com/paper/2601.08026/) (2026)
- [Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature](https://mlsanity.com/paper/2607.23886/) (2026)

## Abstract (arXiv)

Scientific literature contains large volumes of complex, unstructured figures that are compound in nature (i.e. composed of multiple images, graphs, and drawings). Separation of these compound figures is critical for information retrieval from these figures. In this paper, we propose a new strategy for compound figure separation, which decomposes the compound figures into constituent subfigures while preserving the association between the subfigures and their respective caption components. We propose a two-stage framework to address the proposed compound figure separation problem. In particular, the subfigure label detection module detects all subfigure labels in the first stage. Then, in the subfigure detection module, the detected subfigure labels help to detect the subfigures by optimizing the feature selection process and providing the global layout information as extra features. Extensive experiments are conducted to validate the effectiveness and superiority of the proposed framework, which improves the detection precision by 9%.