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Robust Lightweight Facial Expression Recognition Network with Label Distribution Training
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add local-feature extractor (ablation) | ShuffleNet-V2 baseline · RAF-DB | accuracy: 82.23 -> 83.57 | Helped |
| Add channel-spatial modulator (ablation) | ShuffleNet-V2 baseline · RAF-DB | accuracy: 82.23 -> 83.12 | Helped |
| Add local-feature extractor and channel-spatial modulator (ablation) | ShuffleNet-V2 baseline · RAF-DB | accuracy: 82.23 -> 83.83 | Helped |
| Add local-feature extractor (ablation) | ShuffleNet-V2 baseline · CAER-S | accuracy: 79.52 -> 80.68 | Helped |
| Add channel-spatial modulator (ablation) | ShuffleNet-V2 baseline · CAER-S | accuracy: 79.52 -> 80.48 | Helped |
| Add local-feature extractor and channel-spatial modulator (ablation) | ShuffleNet-V2 baseline · CAER-S | accuracy: 79.52 -> 81.48 | Helped |
| Train with generated label distributions instead of single labels (ablation) | EfficientFace · RAF-DB | accuracy: 85.66 -> 88.36 | Helped |
| Train with generated label distributions instead of single labels (ablation) | EfficientFace · CAER-S | accuracy: 84.51 -> 85.87 | Helped |
| Train with generated label distributions instead of single labels (ablation) | ShuffleNet-V2 baseline · RAF-DB | accuracy: 84.58 -> 87.87 | Helped |
| Train with generated label distributions instead of single labels (ablation) | ShuffleNet-V2 baseline · CAER-S | accuracy: 84.06 -> 85.74 | Helped |
Closest papers by meaning
- Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition (CVPR 2020 · similarity 0.87)
- Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition (AAAI 2023 · similarity 0.87)
- Open-Set Facial Expression Recognition (AAAI 2024 · similarity 0.87)
- Navigating Label Ambiguity for Facial Expression Recognition in the Wild (AAAI 2025 · similarity 0.86)
- FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract) (AAAI 2024 · similarity 0.85)
- Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition (AAAI 2024 · similarity 0.85)
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