21 citations · 37 across the 4 of their papers we have counts for
6 papers
Cuid: A new study of perceived image quality and its subjective assessment
Lucie Lévêque, Ji Yang, Xiaohan Yang +5
Research on image quality assessment (IQA) remains limited mainly due to our incomplete knowledge about human visual perception. Existing IQA algorithms have been designed or train…
MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment
Hancheng Zhu, Leida Li, Jinjian Wu +2
Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable suc…
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Sicheng Zhao, Zizhou Jia, Hui Chen +3
Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these…
Incremental Few-Shot Learning for Pedestrian Attribute Recognition
Liuyu Xiang, Xiaoming Jin, Guiguang Ding +2
Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a f…
Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval
Xinxun Xu, Hao Wang, Leida Li +1
Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario. Previous w…
Image Quality Assessment Guided Deep Neural Networks Training
Zhuo Chen, Weisi Lin, Shiqi Wang +2
For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i.e., artifact-free). However, dig…