April 4, 2024, 4:45 a.m. | Huayi Zhou, Fei Jiang, Hongtao Lu

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.02544v1 Announce Type: new
Abstract: Existing head pose estimation datasets are either composed of numerous samples by non-realistic synthesis or lab collection, or limited images by labor-intensive annotating. This makes deep supervised learning based solutions compromised due to the reliance on generous labeled data. To alleviate it, we propose the first semi-supervised unconstrained head pose estimation (SemiUHPE) method, which can leverage a large amount of unlabeled wild head images. Specifically, we follow the recent semi-supervised rotation regression, and focus on …

arxiv cs.cv head semi-supervised type

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