- 06/2020. Andrew Ng's deeplearning.ai has a blog post highlighting our joint ACL 2020 paper with Salesforce Research on Double-Hard Debias [link].
- 06/2020. TechXplore features our work on regulation of Face Recognition in Researchers call for new federal authority to regulate facial recognition tech.
- 05/2020. With a group of colleagues and funding from the MacArthur Foundation, we released the whitepaper Face Recognition Technologies in the Wild: A Call for a Federal Office.
- 05/2020. Our group recently received a Facebook Research Award 2020 and gift funding from eBay Research and Adobe Research.
- 04/2020. Our vislang.ai website is up and running entirely on the cloud!
- 04/2020. New papers accepted at CVPR 2020, ACL 2020, and ICSE 2020.
- 02/2020. Co-Organizing with colleagues at the Seoul National University and other places, the 2nd workshop on Video Turing Test: Toward Human-Level Video Story Understanding at ECCV 2020. Send your submissions to the DramaQA Challenge and attend our workshop!
- 07/2019. Posts from NVIDIA [link] and IBM Research [link] about Text2Scene
- 07/2019. Text2Scene gets named among 45 Best CVPR Paper Finalists among 1,294 accepted papers (top 1% of all submissions) [link]
- 05/2019. UVA Today features work by PhD student Tianlu Wang under her Presidential Fellowship Using Data Science to determine why one job candidate beats out another.
- 05/2019. Participated at the Ethics in AI Panel at Escape Velocity 2019 that took place in Washington DC's National Harbor.
- 04/2019. PhD Student Tianlu Wang gave a talk at the TomTom Applied Machine Learning Conference.
- 09/2018. Co-organizing and Participating in the panel on Dealing with Bias and Unfairness in ML at the ACM Richard Tapia Celebration of Diversity in Computing, Orlando, FL.
- 02/2018. Received a Google Faculty Research Award and an IBM Faculty Award.
- 08/2017. Our work at UVA with UCLA's NLP Group gets coverage in WIRED, Daily Mail, The Times of London, Glamour, Bloomberg, among others.
- 09/2017. We obtained a Best --Long-- Paper Award at EMNLP 2017~!
With colleagues Erik-Learned Miller, Jamie Morgenstern, and Joy Buolamwini we make the case that benchmarks fall short in assessing Face Recognition Technologies (FRTs) for deployment and we propose a more holistic approach.
This demo attemps to make it difficult for a model to predict gender from an image by modifying it so that this task becomes harder while retaining most image information.