Reproduce “TopoFR: A Closer Look at Topology Alignment on Face Recognition” on OpenResearch
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About this paper
Jun Dan, Yang Liu, Jiankang Deng, Haoyu Xie, Siyuan Li, Baigui Sun, Shan Luo
The TopoFR framework enhances face recognition models by explicitly preserving the topological structure of input data within the latent space, leveraging a perturbation-guided alignment strategy and adaptive hard sample mining. It demonstrated superior performance across multiple benchmarks, including a second-place finish on the ICCV21 MFR-Ongoing challenge.
Key insights
- The topological structure of input data is frequently lost or distorted during deep FR model training, negatively impacting model generalization.
- Direct topological alignment causes 'structure collapse'; a perturbation-guided approach is essential to maintain meaningful structural diversity and prevent overfitting.
- Adaptive hard sample mining, considering both prediction uncertainty and accuracy, effectively mitigates structural damage in the latent space and improves overall structure preservation.
Summary by alphaXiv.
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