64 citations · 103 across the 9 of their papers we have counts for
9 papers
A biological vision inspired framework for machine perception of abutting grating illusory contours
Xiao Zhang, Kai-Fu Yang, Xian-Shi Zhang +3
Higher levels of machine intelligence demand alignment with human perception and cognition. Deep neural networks (DNN) dominated machine intelligence have demonstrated exceptional…
Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting
Wang-Wang Yu, Xian-Shi Zhang, Fu-Ya Luo +4
Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation. Meanwhile, video-level spotting lacks sufficient infor…
Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment
Yijun Cao, Xianshi Zhang, Fuya Luo +2
PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with nea…
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays
Xinxu Wei, Haohan Bai, Xianshi Zhang +1
Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia. With the emergence of deep learning, the identification accuracy of pneumonia ha…
Learning to Adapt to Light
Kai-Fu Yang, Cheng Cheng, Shi-Xuan Zhao +2
Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image. There are multiple light-related tasks (for example, low-light enha…
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment
Yijun Cao, Xianshi Zhang, Fuya Luo +2
Recent visual odometry (VO) methods incorporating geometric algorithm into deep-learning architecture have shown outstanding performance on the challenging monocular VO task. Despi…
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement
Xinxu Wei, Xianshi Zhang, Shisen Wang +2
Low-light image enhancement is a challenging low-level computer vision task because after we enhance the brightness of the image, we have to deal with amplified noise, color distor…
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
Xinxu Wei, Xianshi Zhang, Shisen Wang +4
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color distortion, unknown nois…
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
Xinxu Wei, Xianshi Zhang, Shisen Wang +4
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color bias, unknown noise, det…