# Semi-supervised Human Pose Estimation in Art-historical Images

arXiv: https://arxiv.org/abs/2207.02976 · HTML: https://mlsanity.com/paper/2207.02976/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 58 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 30 of 33)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [Pose Recognition with Cascade Transformers](https://mlsanity.com/paper/2104.06976/) (2021)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [Proposal Learning for Semi-Supervised Object Detection](https://mlsanity.com/paper/2001.05086/) (2020)
- [Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection](https://mlsanity.com/paper/1803.09867/) (2018)
- [Detecting People in Artwork with CNNs](https://mlsanity.com/paper/1610.08871/) (2016)
- [Improving Object Detection in Art Images Using Only Style Transfer](https://mlsanity.com/paper/2102.06529/) (2021)
- [Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning](https://mlsanity.com/paper/2012.05616/) (2020)
- [The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs](https://mlsanity.com/paper/1505.00110/) (2015)
- [The iMet Collection 2019 Challenge Dataset](https://mlsanity.com/paper/1906.00901/) (2019)

## Built on by (most cited first, 6 of 6)

- [Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History](https://mlsanity.com/paper/2301.05124/) (2023)
- [SniffyArt: The Dataset of Smelling Persons](https://mlsanity.com/paper/2311.11888/) (2023)
- [Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art Exploration](https://mlsanity.com/paper/2403.19174/) (2024)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training](https://mlsanity.com/paper/2402.11566/) (2024)
- [ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification](https://mlsanity.com/paper/2408.01827/) (2024)

## Abstract (arXiv)

Gesture as language of non-verbal communication has been theoretically established since the 17th century. However, its relevance for the visual arts has been expressed only sporadically. This may be primarily due to the sheer overwhelming amount of data that traditionally had to be processed by hand. With the steady progress of digitization, though, a growing number of historical artifacts have been indexed and made available to the public, creating a need for automatic retrieval of art-historical motifs with similar body constellations or poses. Since the domain of art differs significantly from existing real-world data sets for human pose estimation due to its style variance, this presents new challenges. In this paper, we propose a novel approach to estimate human poses in art-historical images. In contrast to previous work that attempts to bridge the domain gap with pre-trained models or through style transfer, we suggest semi-supervised learning for both object and keypoint detection. Furthermore, we introduce a novel domain-specific art data set that includes both bounding box and keypoint annotations of human figures. Our approach achieves significantly better results than methods that use pre-trained models or style transfer.