MECO is a multimodal dataset of 38 hours of video, audio, EEG, and ECG data from 42 older adults annotated for emotional states and cognitive scores.
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FairST combines 1D/2D/3D convolutions with fairness regularization using novel region-based and individual-based fairness gap metrics, reducing fairness gaps over 80% while improving accuracy over LSTMs, ConvLSTMs, and 3D CNNs on bike and ride share data.
HST-HGN uses heterogeneous spatial-temporal hypergraph networks combined with bidirectional Mamba state space models to achieve state-of-the-art driver fatigue assessment from untrimmed videos while maintaining computational efficiency for real-time use.
The authors achieve third place in EmotiW 2019 engagement intensity regression by extending an LSTM framework with facial landmarks, rank loss, and bootstrap aggregation to reach MSE 0.0626 on the test set.
citing papers explorer
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MECO: A Multimodal Dataset for Emotion and Cognitive Understanding in Older Adults
MECO is a multimodal dataset of 38 hours of video, audio, EEG, and ECG data from 42 older adults annotated for emotional states and cognitive scores.
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FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems
FairST combines 1D/2D/3D convolutions with fairness regularization using novel region-based and individual-based fairness gap metrics, reducing fairness gaps over 80% while improving accuracy over LSTMs, ConvLSTMs, and 3D CNNs on bike and ride share data.
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HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment
HST-HGN uses heterogeneous spatial-temporal hypergraph networks combined with bidirectional Mamba state space models to achieve state-of-the-art driver fatigue assessment from untrimmed videos while maintaining computational efficiency for real-time use.
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Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression
The authors achieve third place in EmotiW 2019 engagement intensity regression by extending an LSTM framework with facial landmarks, rank loss, and bootstrap aggregation to reach MSE 0.0626 on the test set.