RL-AWB uses reinforcement learning to optimize parameters of a statistical white-balance estimator for nighttime scenes and reports better generalization on a new multi-sensor dataset.
Journal of Machine Learning Research21(181), 1–50 (2020)
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A self-paced curriculum learning module with dual-level difficulty scoring improves weighted F1 scores by 1.2-10.4% when added to existing multimodal emotion recognition models on IEMOCAP and MELD.
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RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes
RL-AWB uses reinforcement learning to optimize parameters of a statistical white-balance estimator for nighttime scenes and reports better generalization on a new multi-sensor dataset.
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Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition
A self-paced curriculum learning module with dual-level difficulty scoring improves weighted F1 scores by 1.2-10.4% when added to existing multimodal emotion recognition models on IEMOCAP and MELD.