The development of robotic avatars could benefit from an improvement in how computers detect objects in low-resolution images. A team at RIKEN has improved computer vision recognition capabilities by training algorithms to better identify objects in low-resolution images. Inspired by human brain
Self-driving cars could benefit from enhanced object detection by using self-supervised learning.
A team at RIKEN has improved computer vision recognition capabilities by training algorithms to better identify objects in low-resolution images. Inspired by human brain memory formation techniques, the model degrades the quality of high-resolution images to train the algorithm in self-supervised learning, enhancing object recognition in low-quality images.
Lin Gu and Tatsuya Harada led a team that has developed an AI algorithm capable of improving computer-aided object detection in self-driving cars and cybernetic avatars. Credit: © 2023 RIKEN “In typical self-supervised learning methods, training data is modified by either masking part of the image or changing contrast before learning the supervisory signal,” explains Gu. “We propose using resolution as a self-supervision clue for the first time.”Aside from typical computer vision uses, Gu notes that perceptual constant representation will be a fundamental part of technologies related to cyborgs and avatars.
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