# GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph

arXiv: https://arxiv.org/abs/2105.11852 · HTML: https://mlsanity.com/paper/2105.11852/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 16 of 16)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs](https://mlsanity.com/paper/1803.08035/) (2018)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [Low-shot learning with large-scale diffusion](https://mlsanity.com/paper/1706.02332/) (2017)
- [Optimization of Graph Neural Networks with Natural Gradient Descent](https://mlsanity.com/paper/2008.09624/) (2020)
- [Context-Aware Embeddings for Automatic Art Analysis](https://mlsanity.com/paper/1904.04985/) (2019)
- [OD-GCN: Object Detection Boosted by Knowledge GCN](https://mlsanity.com/paper/1908.04385/) (2019)
- [Historical and Modern Features for Buddha Statue Classification](https://mlsanity.com/paper/1909.12921/) (2019)

## Built on by (most cited first, 11 of 11)

- [StyleFool: Fooling Video Classification Systems via Style Transfer](https://mlsanity.com/paper/2203.16000/) (2022)
- [Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis](https://mlsanity.com/paper/2304.10278/) (2023)
- [ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques](https://mlsanity.com/paper/2308.15284/) (2023)
- [Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data](https://mlsanity.com/paper/2404.12569/) (2024)
- [A deep learning approach to clustering visual arts](https://mlsanity.com/paper/2106.06234/) (2021)
- [Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations](https://mlsanity.com/paper/2409.08245/) (2024)
- [VL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings](https://mlsanity.com/paper/2603.02435/) (2026)
- [Integrating Contextual Knowledge to Visual Features for Fine Art Classification](https://mlsanity.com/paper/2105.15028/) (2021)
- [ExpNet: A unified network for Expert-Level Classification](https://mlsanity.com/paper/2211.15672/) (2022)
- [Built Year Prediction from Buddha Face with Heterogeneous Labels](https://mlsanity.com/paper/2109.00812/) (2021)
- [Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition](https://mlsanity.com/paper/2403.05703/) (2024)

## Abstract (arXiv)

The rise of digitization of cultural documents offers large-scale contents, opening the road for development of AI systems in order to preserve, search, and deliver cultural heritage. To organize such cultural content also means to classify them, a task that is very familiar to modern computer science. Contextual information is often the key to structure such real world data, and we propose to use it in form of a knowledge graph. Such a knowledge graph, combined with content analysis, enhances the notion of proximity between artworks so it improves the performances in classification tasks. In this paper, we propose a novel use of a knowledge graph, that is constructed on annotated data and pseudo-labeled data. With label propagation, we boost artwork classification by training a model using a graph convolutional network, relying on the relationships between entities of the knowledge graph. Following a transductive learning framework, our experiments show that relying on a knowledge graph modeling the relations between labeled data and unlabeled data allows to achieve state-of-the-art results on multiple classification tasks on a dataset of paintings, and on a dataset of Buddha statues. Additionally, we show state-of-the-art results for the difficult case of dealing with unbalanced data, with the limitation of disregarding classes with extremely low degrees in the knowledge graph.