Papers › A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression...
A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition
Shu Liu, Yan Xu, Tongming Wan, Xiaoyan Kui
Facial expression recognition (FER) plays a significant role in our daily life. However, annotation ambiguity in the datasets could greatly hinder the performance. In this paper, we address FER task via label distribution learning paradigm, and develop a dual-branch Adaptive Distribution Fusion (Ada-DF) framework. One auxiliary branch is constructed to obtain the label distributions of samples. The class distributions of emotions are then computed through the label distributions of each emotion. Finally, those two distributions are adaptively fused according to the attention weights to train the target branch. Extensive experiments are conducted on three real-world datasets, RAF-DB, AffectNet and SFEW, where our Ada-DF shows advantages over the state-of-the-art works. The code is available at https://github.com/taylor-xy0827/Ada-DF.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Expression Recognition (FER) | AffectNet | Ada-DF | Accuracy (7 emotion) | 65.34 | #42 of 50 | Archive leaderboard | report |
| Facial Expression Recognition (FER) | RAF-DB | Ada-DF | Overall Accuracy | 90.04 | #17 of 35 | Archive leaderboard | report |
| Facial Expression Recognition (FER) | SFEW | Ada-DF | Accuracy | 60.46 | #1 of 4 | 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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