Detecting people in Cubist art

By Shiry Ginosar, Daniel Haas, Timothy Brown, Jitendra Malik. 2014

50 citations recorded by the source corpus.

Detectors trained on natural images can detect parts that characterize person figures in Cubist paintings.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Ali Farhadi
2. Ross B. Girshick
3. Adam Van Etten

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ali Farhadi
4. Xiaogang Wang
5. Chunhua Shen
6. Trevor Darrell
7. Abhinav Kumar Gupta
8. Cordelia Schmid

Citations in the corpus

• You Only Look Once: Unified, Real-Time Object Detection (2015, methodology): “We compare YOLO to other detection systems on the Picasso Dataset [12] and the People-Art Dataset [3], two datasets for testing person detection on artwork.”
• You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery (2018, methodology): “…greatest inference speed and highest score on the PASCAL VOC dataset. authors also showed that this framework is highly transferrable to new domains by demonstrating superior performance to other frameworks ( i . e ., SSD and Faster R-CNN) on the Picasso Dataset [3] and the People-Art Dataset [1].”
• The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs (2015, background): “Ginosar et al [28] use deep learning for detecting people in Picasso paintings, achieving rates of about 10%.”