The paper introduces a framework for joint estimation of ordinal and nominal face attributes using multi-task learning. By sharing low-level parameters and designing separate classifiers, the approach simplifies the task of ordinal attribute estimation. The use of homoskedastic uncertainty to optimize loss weights among multiple tasks is a key contribution. Experimental results demonstrate superior performance compared to existing methods in terms of accuracy on benchmarks with multiple face attributes. The proposed approach is feasible for edge systems, addressing bias issues in face attribute estimation.
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by Huaqing Yuan... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00561.pdfDeeper Inquiries