Research map / Label-efficient and robust learning / Recognition and long-tail
Using Ranking-CNN for Age Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ordinal binary rankers and aggregate their outputs instead of multi-class CNN classification | CNN age estimation · MORPH Album 2 | MAE: 3.65 -> 2.96 | Helped |
| Use Ranking-CNN features with Ranking-SVM instead of the full Ranking-CNN estimator | Ranking-CNN · MORPH Album 2 | MAE: 2.96 -> 3.63 | Hurt |
| Use Ranking-CNN instead of MR-CNN | Age estimation · MORPH | MAE: 3.27 -> 2.96 | Helped |
| Use Ranking-CNN instead of DEX | Age estimation · MORPH | MAE: 3.25 -> 2.96 | Helped |
| Use Ranking-CNN instead of OR-CNN | Age estimation · MORPH | MAE: 3.34 -> 2.96 | Helped |
| Use Ranking-CNN instead of engineered-feature baselines | Age estimation · MORPH Album 2 | MAE: 4.88 -> 2.96 | Helped |
| Use Ranking-CNN instead of CNN features with SVM | Age estimation · MORPH Album 2 | MAE: 3.95 -> 2.96 | Helped |
| Use Ranking-CNN for age estimation | Age estimation · MORPH Album 2 | CS (L=6): 89.90% | Helped |
| Use Ranking-CNN for age estimation | Age estimation · MORPH Album 2 | CS (L=7): 92.93% | Helped |
| Use Ranking-CNN features with Ranking-SVM instead of BIF+OLPP with Ranking-SVM | Age estimation · MORPH Album 2 | MAE: 5.03 -> 3.63 | Helped |
Closest papers by meaning
- Ordinal Regression With Multiple Output CNN for Age Estimation (CVPR 2016 · similarity 0.91)
- Deep Regression Forests for Age Estimation (CVPR 2018 · similarity 0.87)
- Order Learning and Its Application to Age Estimation (ICLR 2020 · similarity 0.87)
- Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition (CVPR 2016 · similarity 0.87)
- 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 0.85)
- Ranking-aware adapter for text-driven image ordering with CLIP (ICLR 2025 · similarity 0.83)
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