Papers › BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
Wanhua Li, Jiwen Lu, Jianjiang Feng, Chunjing Xu, Jie zhou, Qi Tian
Age estimation is an important yet very challenging problem in computer vision. Existing methods for age estimation usually apply a divide-and-conquer strategy to deal with heterogeneous data caused by the non-stationary aging process. However, the facial aging process is also a continuous process, and the continuity relationship between different components has not been effectively exploited. In this paper, we propose BridgeNet for age estimation, which aims to mine the continuous relation between age labels effectively. The proposed BridgeNet consists of local regressors and gating networks. Local regressors partition the data space into multiple overlapping subspaces to tackle heterogeneous data and gating networks learn continuity aware weights for the results of local regressors by employing the proposed bridge-tree structure, which introduces bridge connections into tree models to enforce the similarity between neighbor nodes. Moreover, these two components of BridgeNet can be jointly learned in an end-to-end way. We show experimental results on the MORPH II, FG-NET and Chalearn LAP 2015 datasets and find that BridgeNet outperforms the state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Age Estimation | ChaLearn 2015 | BridgeNet | MAE | 2.87 | #2 of 7 | Archive leaderboard | report |
| Age Estimation | ChaLearn 2015 | BridgeNet | e-error | 0.255140 | #2 of 7 | Archive leaderboard | report |
| Age Estimation | FGNET | BridgeNet | MAE | 2.56 | #2 of 8 | Archive leaderboard | report |
| Age Estimation | MORPH album2 (Caucasian) | BridgeNet | MAE | 2.38 | #7 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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