Research map / Label-efficient and robust learning / Recognition and long-tail
EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed algorithm to annotate high-confidence AU predictions | EmotioNet images in the wild · EmotioNet images, top 1/3 | true-positive fraction: 80.9% | Helped |
| Use lower-confidence predictions rather than the top 1/3 | EmotioNet images in the wild · EmotioNet images, middle 1/3 | true-positive fraction: 80.9% -> 74.9% | Hurt |
| Use lowest-confidence predictions rather than the top 1/3 | EmotioNet images in the wild · EmotioNet images, bottom 1/3 | true-positive fraction: 80.9% -> 67.2% | Hurt |
Closest papers by meaning
- EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition (NeurIPS 2025 · similarity 0.90)
- Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis (AAAI 2026 · similarity 0.90)
- AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models (ICML 2025 · similarity 0.88)
- FindingEmo: An Image Dataset for Emotion Recognition in the Wild (NeurIPS 2024 · similarity 0.88)
- A Unified and Interpretable Emotion Representation and Expression Generation (CVPR 2024 · similarity 0.88)
- OUS: Bridging Scene Context and Facial Features to Overcome the Rigid Cognitive Problem (AAAI 2025 · similarity 0.88)
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