Research map / Label-efficient and robust learning / Recognition and long-tail
Recurrent Face Aging
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Generate aging through recurrent intermediate face states instead of a one-step transformation | RFA · Face aging | visual realism and ghosting artifacts | Helped |
| Use recurrent intermediate states for face aging | RFA · Face aging compared with CDL and FT Demo | visual realism | Mixed |
| Use RFA rather than CDL for age progression | Face aging · FG-NET qualitative comparison | target-age characteristics | Mixed |
| Use RFA-generated aged faces in place of younger faces | Deep CNN face verification · FG-NET cross-age face verification | FAR-FRR performance | Helped |
Closest papers by meaning
- Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines (CVPR 2016 · similarity 0.90)
- Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition (AAAI 2019 · similarity 0.89)
- Continuous Face Aging via Self-Estimated Residual Age Embedding (CVPR 2021 · similarity 0.88)
- From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging (NeurIPS 2025 · similarity 0.88)
- A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark (CVPR 2024 · similarity 0.83)
- Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors (ICLR 2025 · similarity 0.83)
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