March 14, 2024, 4:45 a.m. | Minsoo Kim, Gi Pyo Nam, Haksub Kim, Haesol Park, Ig-Jae Kim

cs.CV updates on

arXiv:2403.08256v1 Announce Type: new
Abstract: In the realm of face image quality assesment (FIQA), method based on sample relative classification have shown impressive performance. However, the quality scores used as pseudo-labels assigned from images of classes with low intra-class variance could be unrelated to the actual quality in this method. To address this issue, we present IG-FIQA, a novel approach to guide FIQA training, introducing a weight parameter to alleviate the adverse impact of these classes. This method involves estimating …

abstract arxiv assessment class classification face guidance however image images labels low performance quality robust sample through type variance

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