Workshop Schedule

Our workshop will be located at CVPR 2019 on Monday June 17th at the Hyatt Regency Hotel, 200 S Pine Ave, Long Beach, room Seaview A. Please see http://cvpr2019.thecvf.com/ for more information on CVPR. The poster session, however, will be at the Pacific Arena Ballroom (main convention center). The poster boards for our workshop are from #30-43.

The tentative schedule is below, details will follow.

8:30am – 8:35am Introduction

8:35am – 8:55am Injoluwa Deborah Raji: Actionable Auditing

8:55am – 9:15am Oral session 1 (2 speakers)

• Terrance De Vries et al. Does Object Recognition Work for Everyone? [PDF]
• Linda Wang and Alexander Wong, Implications of Computer Vision Driven Assistive Technologies Towards Individuals with Visual Impairment. [PDF]

9:15am – 9:35am Cewu Lu: Data Protection in China

9:35am – 10:25am Poster session + coffee break

10:25am – 10:45am Laura Moy: Beware of (Mis)users: Anticipating Uninformed or Irresponsible Users Operating in an Unfair Context

10:45am – 11:05am Oral session 2 (2 speakers)

• Tianlu Wang et al. Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations. [PDF]
• Misha Benjamin et al. Towards Standardization of Data Licenses: The Montreal Data License. [PDF]

11:05am – 11:35am Morgan Klaus Scheuerman: Implications of Gendered Infrastructures in Computer Vision

11:35am – 11:55am Kate Saenko: Dataset Bias and How to Deal with It

11:55am – 12:30pm Panel session

Accepted papers

[1] Linda Wang and Alexander Wong, Implications of Computer Vision Driven Assistive Technologies Towards Individuals with Visual Impairment. [PDF]

[2] Terrance De Vries, Ishan Misra, Changhan Wang and Laurens van der Maaten, Does Object Recognition Work for Everyone? [PDF]

[3] Danna Gurari, Qing Li, Chi Lin, Yinan Zhao, Anhong Guo, Abigale Stangl and Jeffrey Bigham, VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People. [PDF]

[4] Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang and Vicente Ordonez, Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations. [PDF]

[5] Kihyuk Sohn, Wenling Shang, Xiang Yu and Manmohan Chandraker, Unsupervised Domain Adaptation for Distance Metric Learning. [PDF]

[6] Chris Dulhanty and Alexander Wong, Auditing ImageNet: Towards A Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets. [PDF]

[7] Emily Denton, Ben Hutchinson, Margaret Mitchell and Timnit Gebru, Detecting Bias with Generative Counterfactual Face Attribute Augmentation. [PDF]

[8] Raymond Bond, Ansgar Koene, Alan Dix, Jennifer Boger, Maurice Mulvenna, Mykola Galushka, Bethany Waterhouse-Bradley, Fiona Browne, Hui Wang and Alexander Wong, Democratisation of Usable Machine Learning in Computer Vision. [PDF]

[9] Benjamin Wilson, Judy Hoffman and Jamie Morgenstern, Predictive Inequity in Object Detection. [PDF]

[10] Misha Benjamin, Paul Gagnon, Negar Rostamzadeh, Chris Pal, Yoshua Bengio and Alex Shee, Towards Standardization of Data Licenses: The Montreal Data License. [PDF]

[11] Aythami Morales, Julian Fierrez and Ruben Vera-Rodriguez, SensitiveNets: Unlearning Undesired Information for Generating Agnostic Representations with Application to Face Recognition. [PDF]

[12] Mohammed Khalil, Habib Ayad and Abdellah Adib, How to protect patient privacy in automated medical diagnosis systems? [PDF]

[13] Martim Brandao, Age and gender bias in pedestrian detection algorithms. [PDF]

[14] Kaylen Pfisterer, Jennifer Boger and Alexander Wong, Food for thought: Ethical considerations of user trust in computer vision. [PDF]