When we take a picture in the real world, the image is always constrained by the lens. If part of a person’s face falls outside the frame, the camera cannot capture it. Existing object detection systems are designed around that limitation, so that if something is not visible, they do not detect it. “But the real object will not just disappear by going out of the lens,” Changlin rightly points out. “There are still potential applications and value in detecting objects outside the frame.” That idea became the motivation for this project. The question first came from his supervisor, Dylan 4 DAILY WACV Monday Oral Presentation Changlin Song is a first-year PhD student at the Australian National University. His paper explores a question that most object detection systems avoid: what happens to an object when it moves outside the camera’s field of view? Changlin tells us how a conversation with his supervisor led him to rethink what detection really means. Extreme Amodal Face Detection
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