181 citations · 309 across the 10 of their papers we have counts for
10 papers
Multi-view Feature Augmentation with Adaptive Class Activation Mapping
Xiang Gao, Yingjie Tian, Zhiquan Qi
We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. Diffe…
Visual Anomaly Detection for Images: A Survey
Jie Yang, Ruijie Xu, Zhiquan Qi +1
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention…
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection
Kai Li, Jie Yang, Siwei Ma +4
Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other facto…
Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function
Kai Li, Bo Wang, Yingjie Tian +1
Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function…
Two-stage Training for Learning from Label Proportions
Jiabin Liu, Bo Wang, Xin Shen +2
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utiliz…
DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation
Jie Yang, Yong Shi, Zhiquan Qi
Automatic detecting anomalous regions in images of objects or textures without priors of the anomalies is challenging, especially when the anomalies appear in very small areas of t…
Concatenated Attention Neural Network for Image Restoration
Tian YingJie, Wang YiQi, Yang LinRui +1
In this paper, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concaten…
Learning to Incorporate Structure Knowledge for Image Inpainting
Jie Yang, Zhiquan Qi, Yong Shi
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous wo…
s-LWSR: Super Lightweight Super-Resolution Network
Biao Li, Jiabin Liu, Bo Wang +2
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performa…
Learning from Label Proportions with Generative Adversarial Networks
Jiabin Liu, Bo Wang, Zhiquan Qi +2
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level propo…