mlsanityData to 14 Oct 2026

Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints

arXiv 2410.12379 ↗First posted 16 Oct 2024cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

In this work we present a large-scale dataset of textitUkiyo-e woodblock prints. Unlike previous works and datasets in the artistic domain that primarily focus on western art, this paper explores this pre-modern Japanese art form with the aim of broadening the scope for stylistic analysis and to provide a benchmark to evaluate a variety of art focused Computer Vision approaches. Our dataset consists of over 175.000 prints with corresponding metadata (eg artist, era, and creation date) from the 17th century to present day. By approaching stylistic analysis as a Multi-Task problem we aim to more efficiently utilize the available metadata, and learn more general representations of style. We show results for well-known baselines and state-of-the-art multi-task learning frameworks to enable future comparison, and to encourage stylistic analysis on this artistic domain.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

23 of 44 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2020End-to-End Object Detection with Transformerscited by 7,414
  5. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  6. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  7. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  8. 2014How transferable are features in deep neural networks?cited by 1,225
  9. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  10. 2021ImageNet-21K Pretraining for the Massescited by 613
  11. 2018End-to-End Multi-Task Learning with Attentioncited by 534
  12. 2016Cross-stitch Networks for Multi-task Learningcited by 467
11 more
  1. 2020MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learningcited by 157
  2. 2018NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reductioncited by 127
  3. 2020Learning to Branch for Multi-Task Learningcited by 115
  4. 2013Recognizing Image Stylecited by 64
  5. 2019Many Task Learning with Task Routingcited by 50
  6. 2016Detecting People in Artwork with CNNscited by 25
  7. 2020KaoKore: A Pre-modern Japanese Art Facial Expression Datasetcited by 18
  8. 2019Interpretable CNNs for Object Classificationcited by 17
  9. 2020A Data Set and a Convolutional Model for Iconography Classification in Paintingscited by 17
  10. 2017OmniArt: Multi-task Deep Learning for Artistic Data Analysiscited by 16
  11. 2021Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotationcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Sparse Autoencoders are Topic Modelscited by 3
  2. 2025"Jutters"cited by 0