On the AI4Pain 2026 dataset, Extra Trees with 115 hand-crafted features (macro-F1 0.539) beats deep sequence models (0.465), and pain localization (0.552) is far harder than pain detection (0.815).
Efficient pain recognition via respiration signals: A single cross-attention transformer multi-window fusion pipeline
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A lightweight transformer predicts iconic gesture placement and intensity from text and emotion alone, outperforming GPT-4o on the BEAT2 dataset for real-time robot deployment.
Lightweight transformer fuses raw and spectral fNIRS representations via unified tokenization for competitive pain recognition on the AI4Pain dataset while remaining computationally compact.
citing papers explorer
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An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
On the AI4Pain 2026 dataset, Extra Trees with 115 hand-crafted features (macro-F1 0.539) beats deep sequence models (0.465), and pain localization (0.552) is far harder than pain detection (0.815).
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Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech
A lightweight transformer predicts iconic gesture placement and intensity from text and emotion alone, outperforming GPT-4o on the BEAT2 dataset for real-time robot deployment.
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A Lightweight Transformer for Pain Recognition from Brain Activity
Lightweight transformer fuses raw and spectral fNIRS representations via unified tokenization for competitive pain recognition on the AI4Pain dataset while remaining computationally compact.