243 citations · 322 across the 7 of their papers we have counts for
7 papers
Dual Feature Decoupling for Fine-Grained OOD Detection
Xiaokun Li, Yaping Huang, Qingji Guan
Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detection methods have been devel…
The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector
Caixia Zhou, Yaping Huang, Mengyang Pu +3
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voti…
EDTER: Edge Detection with Transformer
Mengyang Pu, Yaping Huang, Yuming Liu +2
Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually…
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
Mengyang Pu, Yaping Huang, Qingji Guan +1
As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or…
Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation
Rumeng Yi, Yaping Huang, Qingji Guan +2
This paper addresses semi-supervised semantic segmentation by exploiting a small set of images with pixel-level annotations (strong supervisions) and a large set of images with onl…
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
Runsheng Zhang, Yaping Huang, Mengyang Pu +4
TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localizatio…
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
Qingji Guan, Yaping Huang, Zhun Zhong +3
This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy…