Chris Thomas

Ph.D. Student
5404 Sennott Square
Department of Computer Science
University of Pittsburgh

Curriculum Vitae·  Google Scholar


I am a Ph.D. student in the Department of Computer Science at the University of Pittsburgh. My research is in computer vision, a sub-area of the broad field of artificial intelligence. My research interests can be broadly categorized as image generation, visual recognition, and high-level image understanding. I am advised by Professor Adriana Kovashka.

In 2017, I did a research internship at Yahoo! Research in New York City. My mentor was Yale Song. Previously, I worked as part of the Keystone Design Group as part of the five-year NSF Expedition in Computing, Visual Cortex on Silicon.

Selected Projects

Cartoon horse
Artistic Object Recognition by Unsupervised Style Adaptation
(ACCV 2018)
Christopher Thomas and Adriana Kovashka
We present an unsupervised domain adaptation method for artistic domains which outperforms state-of-the-art baselines. We also release a large artistic dataset.
Project Page (contains paper, supplementary material, and dataset).
Faces in advertisements
Persuasive Faces: Generating Faces in Advertisements
(BMVC 2018)
Christopher Thomas and Adriana Kovashka
We model and generate faces which appear to come from different types of ads. Our semantically conditioned model greatly outperforms existing baselines.
Paper and Supplementary Material.
Lewis Hine Photograph (Hunter)
Seeing Behind the Camera: Identifying the Authoriship of a Photograph
(CVPR 2016)
Christopher Thomas and Adriana Kovashka
We propose the novel problem of photographer authoriship classification and provide a large dataset and CNN trained from scratch for this task.
Project Page (contains paper, supplementary material, dataset, and trained CNN).
OpenSALICON Saliency Map
OpenSALICON: An Open Source Implementation of the Salicon Saliency Model
Technical Report (2016)
Christopher Thomas
We provide an open source implementation of the SALICON saliency algorithm, one of the top-performing saliency algorithms on the MIT 300 saliency benchmark.
GitHub Page (contains technical report and code) and Pre-trained Models.