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Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors
May 7, 2024, 4:48 a.m. | Samreen Anjum, Suyog Jain, Danna Gurari
cs.CV updates on arXiv.org arxiv.org
Abstract: We propose a hybrid framework for consistently producing high-quality object tracks by combining an automated object tracker with little human input. The key idea is to tailor a module for each dataset to intelligently decide when an object tracker is failing and so humans should be brought in to re-localize an object for continued tracking. Our approach leverages self-supervised learning on unlabeled videos to learn a tailored representation for a target object that is then …
abstract arxiv automated cs.cv dataset errors framework human human involvement hybrid key object quality self-supervised learning supervised learning the key type
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