and Euro-centric, encoding humanlike biases about race, ethnicity, gender, weight, and more. Previous research has found that ImageNet and OpenImages - two large, publicly available image datasets - are U.S. In computer vision datasets, poor representation can result in harm, particularly given that the AI field generally lacks clear descriptions of bias. The data was de-identified in a three-step process that involved human review of all video files, automated reviews, and a human review of automated blurring, Facebook says - excepting for participants who consented to share their audio and unblurred faces. Some footage was paired with 3D scans, motion data from inertial measurement units, and eye tracking. Ego4D captures where the camera wearer chose to gaze at in a specific environment, what they did with their hands (and objects in front of them), and how they interacted with other people from an egocentric perspective. The teams had participants record roughly eight-minute clips of day-to-day scenarios like grocery shopping, cooking, talking while playing games, and engaging in group activities with family and friends. King Abdullah University of Science and Technology.International Institute of Information Technology.For example, if you strap a computer vision system onto a rollercoaster, it likely won’t have any idea what it’s looking at - even if it’s trained on hundreds of thousands of images or videos of rollercoasters shown from the sidelines on the ground.Įgo4D recruited teams at partner universities to hand out off-the-shelf, head-mounted cameras (including GoPros, ZShades, and WeeViews) and other wearable sensors to research participants so that they could capture first-person, unscripted videos of their daily lives. Collecting the dataĪccording to Kristen Grauman, lead research scientist at Facebook, today’s computer vision systems don’t relate to first- and third-person perspectives in the same way that people do. And as a supplement to the work, researchers from Facebook Reality Labs (Facebook’s AR- and VR-focused research division) used Vuzix Blade smartglasses to collect an additional 400 hours of first-person video data in staged environments in research labs. Facebook funded the project through academic grants to each of the participating universities. To that end, Ego4D brings together a consortium of universities and labs across nine countries, which collected more than 2,200 hours of first-person video featuring over 700 participants in 73 cities going about their daily lives.
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