Research map / Video / Video segmentation and tracking
Automatic Face Aging in Videos via Deep Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the RL neighbor-selection policy (ablation) | video age progression · AGFW-v2 video set | Aging consistency: 346.25 -> 245.64 | Helped |
| add the RL neighbor-selection policy (ablation) | video age progression · AGFW-v2 video set | Temporal smoothness: 75.7 -> 61.80 | Helped |
| add the RL neighbor-selection policy (ablation) | video age progression · AGFW-v2 video set | Matching accuracy: 78.06% -> 83.67% | Helped |
| synthesize age-progressed frames | original video frames · AGFW-v2 video set | Matching accuracy: 60.61% -> 83.67% | Helped |
Closest papers by meaning
- Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines (CVPR 2016 · similarity 0.89)
- Age Progression and Regression with Spatial Attention Modules (AAAI 2020 · similarity 0.88)
- Continuous Face Aging via Self-Estimated Residual Age Embedding (CVPR 2021 · similarity 0.88)
- Learning Face Age Progression: A Pyramid Architecture of GANs (CVPR 2018 · similarity 0.88)
- From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging (NeurIPS 2025 · similarity 0.86)
- 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 0.82)
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