38 citations · 108 across the 8 of their papers we have counts for
9 papers
Real-Time Super-Resolution for Real-World Images on Mobile Devices
Jie Cai, Zibo Meng, Jiaming Ding +1
Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts. Although recent progress in ISR has been…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +43
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world settin…
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
Jie Cai, Zibo Meng, Chiu Man Ho
Image super-resolution is one of the important computer vision techniques aiming to reconstruct high-resolution images from corresponding low-resolution ones. Most recently, deep l…
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
Jie Cai, Zibo Meng, Ahmed Shehab Khan +6
Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved fo…
Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network
Jie Cai, Zibo Meng, Ahmed Shehab Khan +4
A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations ca…
Probabilistic Attribute Tree in Convolutional Neural Networks for Facial Expression Recognition
Jie Cai, Zibo Meng, Ahmed Shehab Khan +3
In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g.,…
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Jie Cai, Zibo Meng, Ahmed Shehab Khan +3
Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world s…
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Shizhong Han, Zibo Meng, Zhiyuan Li +4
Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial…