activity
20172022
most citedIncremental Boosting Convolutional Neural Network for Facial Action Unit Recognition

62 citations · 150 across the 12 of their papers we have counts for

collaborators

12 papers

eess.IV20219 cited

Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report

Andrey Ignatov, Andres Romero, Heewon Kim +28

Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions hav…

eess.IV2020

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…

eess.IV202023 cited

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…

eess.IV20202 cited

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…

cs.CV20198 cited

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…

cs.CV201820 cited

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.,…