“In healthcare, military, and security applications, it’s very hard to collect data,” Sarah tells us. “Even if you can collect data, you can’t crowdsource to get labels because they’re very private and secure. Sometimes, it’s even hard to get adjacent domain data. You can’t say I’m sitting on this specific dataset on human behavior, and I’ll just finetune it a bit to bring it to our application.” Bridging the domain gap is challenging. Despite remarkable strides in computer vision for human-centric applications, challenges persist in domains such as infant monitoring and endangered animal studies. The organizers believe addressing these challenges will improve outcomes in these domains and contribute to advancements in related fields facing similar data constraints. “If you’re interested in applied machine learning, then you’re interested in small data machine learning because we’re never going to have the huge datasets we want,” Michael declares. “There’s often still a focus on technical innovation or finding data that fits interesting theory. We do lots of things that have a theoretical grounding in our work, but we’re focusing on domains where it’s critical to solve these problems even though the datasets are so small that we can’t necessarily apply existing well-known small data machine learning tools.” 23 DAILY WACV Friday CV4Smalls Infant Static Pose Generation: The structure of the proposed three-phase infant Static Generative Pose Model: 3D pose estimation, posture-guided generation, and image rendering. The workshop will start with a general discussion on the challenges faced in computer vision with small data, focusing on infants and endangered animals. Then, the day will be split, with the first half related to infant health monitoring and the second dedicated to animal
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