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UNWIND is introduced, a facial-video framework for stress detection that analyzes a complete recording as a single model input, eliminating the need for temporal windowing or external segmentation and showing that complete-recording inference can be performed within a single unified model.
The results show that alternative visual forms of the same EDA signal can provide useful inductive biases for stress detection, and that a compact selection of complementary representations can be more effective than the raw waveform alone.
The findings demonstrate the feasibility of processing structurally different neural and visual signals within a compact unified architecture while highlighting the importance of controlling duration-related cues in affective datasets.
Deep reinforcement learning (DRL) has achieved great success in many simulated and real-world robotic tasks. However, the difficulty of designing efficient and dense reward functions makes applying DRL to tackle complex long-horizon and open-world tasks a great challenge. Generative adversarial imitation learning (GAIL...
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