
[2409.20098] DIG-FACE: De-biased Learning for Generalized Facial ...
2024年9月30日 · We introduce a novel task, Generalized Facial Expression Category Discovery (G-FACE), that discovers new, unseen facial expressions while recognizing known categories …
What is face away countdown dig? - Microsoft Community
2018年4月21日 · What is face away countdown dig? This app shows up running when i try to shut down. Is it some sort of malware? How do i get rid of it? This is due to Cyberlink Youcam …
DIG-FACE: De-biased Learning for Generalized Facial Expression …
To address these challenges, we proposed DIG-FACE, a de-biasing framework that includes implicit debiasing, which minimizes bias through adversarial learning, and explicit debiasing, …
[PDF] DIG-FACE: De-biased Learning for Generalized Facial …
In the implicit debiasing process of DIG-FACE, we devise a novel learning strategy that aims at estimating and minimizing the upper bound of implicit bias. In the explicit debiasing process, …
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Abstract - arXiv.org
G-FACE framework, DIG-FACE, which incorporates both implicit and explicit debiasing strategies. In the implicit debiasing step, we introduce a novel F-discrepancy-based metric to help reveal …
Clarence-CV/G-FACE - GitHub
By addressing the challenges caused by both biases, we propose a Debiased G-FACE method, namely DIG-FACE, that facilitates the de-biasing of both implicit and explicit biases. In the …
microsoft/DigiFace1M - GitHub
The DigiFace-1M dataset is a collection of over one million diverse synthetic face images for face recognition. It was introduced in our paper DigiFace-1M: 1 Million Digital Face Images for Face …
人脸识别合集 | 7 VGGFace解析 - 知乎 - 知乎专栏
三元组损失训练的目的是学习在最终应用中表现良好的分数向量,即通过比较欧几里德空间中的人脸描述符来验证身份。 这在本质上类似于“度量学习 (metric learning)”,并且像许多度量学习 …
DIGI-FACE Platform
DIGI-FACE is an online platform providing teaching, learning, research, and communication opportunities for the African Excellence Centres and its Global network
Papers with Code - Facial Expression Recognition
Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones.
- 某些结果已被删除