no code implementations • 22 Sep 2023 • Xizhe Wang, Yihua Zhong, Changqin Huang, Xiaodi Huang
Empirical results demonstrate that it provides learners with high-quality reading comprehension questions that are broadly aligned with expert-crafted questions at a statistical level.
1 code implementation • 2 Jun 2023 • Xiaoyong Mei, Yi Yang, Ming Li, Changqin Huang, Kai Zhang, Pietro Lió
In this study, we propose a feature reuse framework that guides the step-by-step texture reconstruction process through different stages, reducing the negative impacts of perceptual and adversarial loss.
1 code implementation • Information Sciences 2021 • Qionghao Huang, Changqin Huang, Xizhe Wang, Fan Jiang
In particular, in the low-level feature learning, a grid-wise attention mechanism is proposed to capture the dependencies of different regions from a facial expression image such that the parameter update of convolutional filters in low-level feature learning is regularized.
Ranked #6 on Facial Expression Recognition (FER) on FER+ (using extra training data)
Facial Expression Recognition Facial Expression Recognition (FER)