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Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition
Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects.
RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment
The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR.
An Attention-Driven Approach of No-Reference Image Quality Assessment
The model learning is implemented by a reinforcement strategy, in which the rewards of both tasks guide the learning of the optimal sampling policy to acquire the "task-informative" image regions so that the predictions can be made accurately and efficiently (in terms of the sampling steps).
Multi-Task Learning
No-Reference Image Quality Assessment
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