Naila Murray (Facebook / Research Scientist)
Unsupervised Meta-Domain Adaptation for Instance Retrieval
Cross-domain item retrieval naturally arises in a variety of applications, for example for question answering, or for querying online visual catalogs with consumer images. We focus on the cross-domain retrieval task and illustrate it with the latter scenario, that is, when unconstrained consumer images are used to query for fashion items in a collection of high-quality photographs provided by retailers. To perform this cross-domain task, approaches typically leverage both consumer and shop domains from a given dataset to learn a domain-invariant representation, allowing these images of different nature to be directly compared. When consumer images are not available beforehand, such training is impossible. In this talk, I describe a recent approach to this challenging and yet practical scenario, which leverages representations learned for cross-domain retrieval from another source dataset and adapts them to the target dataset for this particular setting.
Naila Murray obtained a BSE in electrical engineering from Princeton University in 2007. In 2012, she received her Ph.D. from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined Xerox Research Centre Europe in 2013 as a research scientist in the computer vision team, working on topics including fine-grained visual categorization, image retrieval and visual attention. From 2015 to 2019 she led the computer vision team at Xerox Research Centre Europe, and continued to serve in this role after its acquisition and transition to becoming NAVER LABS Europe. In 2019, she became the director of science at NAVER LABS Europe. In 2020, she joined Facebook AI Research as a research engineering manager. She has served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and program chair for ICLR 2021. Her current research interests include continual learning and multi-modal search.