@inproceedings{hamilton2021mosaic,
author = {Hamilton, Mark and Fu, Stephanie and Lu, Mindren and Bui, Johnny and Bopp, Darius and Chen, Zhenbang and Tran, Felix and Wang, Margaret and Rogers, Marina and Zhang, Lei and Hoder, Chris and Freeman, William T.},
title = {MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval},
booktitle = {NeurIPS 2020 Competition and Demonstration Track},
year = {2021},
month = {February},
abstract = {We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia. To create this application, we introduce Conditional Image Retrieval (CIR) which combines visual similarity search with user supplied filters or "conditions". This technique allows one to find pairs of similar images that span distinct subsets of the image corpus. We provide a generic way to adapt existing image retrieval data-structures to this new domain and provide theoretical bounds on our approach's efficiency. To quantify the performance of CIR systems, we introduce new datasets for evaluating CIR methods and show that CIR performs non-parametric style transfer. Finally, we demonstrate that our CIR data-structures can identify "blind spots" in Generative Adversarial Networks (GAN) where they fail to properly model the true data distribution.},
url = {http://approjects.co.za/?big=en-us/research/publication/conditional-image-retrieval/},
}