At Facebook everyday hundreds of millions of users interact with billions of visual contents. By understanding what's in an image, our systems can help connect users with the things that matter most to them. To improve our recognition system, I will talk about two main research challenges: how we train models at the scale of billions, and how we improve the reliability of the model prediction. Since current models are typically trained on data that are individually labeled by human annotators, scaling up to billions is non-trivial. We solve the challenge by training image recognition networks on large sets of public images with user-supplied hashtags as labels. By leveraging weakly supervised pretraining, our best model achieved a record-high 85.4% accuracy on ImageNet dataset.
Yixuan (Sharon) Li is a Research Scientist at Facebook AI, Computer Vision Group. She leads the research effort on large-scale visual learning with high dimensional label space. Before joining Facebook, she obtained her PhD from Cornell University in 2017. Yixuan's research interests are in developing robust, scalable and efficient machine learning algorithms and their applications. She was selected as one of the "Rising Stars in EECS" by Stanford University in 2017. She is the recipient of ACM-Women Scholarship. Previously she spent two summers interning at Google Research Mountain View in 2015 and 2016.