62 citations · 203 across the 19 of their papers we have counts for
20 papers
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection
Huiming Sun, Lan Fu, Jinlong Li +5
Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient…
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…
Pik-Fix: Restoring and Colorizing Old Photos
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +6
Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer…
ROMNet: Renovate the Old Memories
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +5
Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cr…
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…
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…
GIA-Net: Global Information Aware Network for Low-light Imaging
Zibo Meng, Runsheng Xu, Chiu Man Ho
It is extremely challenging to acquire perceptually plausible images under low-light conditions due to low SNR. Most recently, U-Nets have shown promising results for low-light ima…
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
Ping Liu, Yuewei Lin, Zibo Meng +4
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition. Unlike…
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…
Omni-supervised Facial Expression Recognition via Distilled Data
Ping Liu, Yunchao Wei, Zibo Meng +3
Facial expression plays an important role in understanding human emotions. Most recently, deep learning based methods have shown promising for facial expression recognition. Howeve…
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…
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Shizhong Han, Zibo Meng, Ahmed Shehab Khan +1
Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However,…
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…
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Zibo Meng, Shizhong Han, Ping Liu +1
It is challenging to recognize facial action unit (AU) from spontaneous facial displays, especially when they are accompanied by speech. The major reason is that the information is…
Detecting Small Signs from Large Images
Zibo Meng, Xiaochuan Fan, Xin Chen +2
In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of mem…
Listen to Your Face: Inferring Facial Action Units from Audio Channel
Zibo Meng, Shizhong Han, Yan Tong
Extensive efforts have been devoted to recognizing facial action units (AUs). However, it is still challenging to recognize AUs from spontaneous facial displays especially when the…