1 code implementation • CVPR 2023 • Caixia Zhou, Yaping Huang, Mengyang Pu, Qingji Guan, Li Huang, Haibin Ling
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators.
1 code implementation • CVPR 2022 • Mengyang Pu, Yaping Huang, Yuming Liu, Qingji Guan, Haibin Ling
In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches.
1 code implementation • ICCV 2021 • Mengyang Pu, Yaping Huang, Qingji Guan, Haibin Ling
Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages.
no code implementations • 26 Mar 2021 • Rumeng Yi, Yaping Huang, Qingji Guan, Mengyang Pu, Runsheng Zhang
In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss.
1 code implementation • 28 Jun 2020 • Zhihao Liu, Hui Yin, Yang Mi, Mengyang Pu, Song Wang
In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data.
1 code implementation • 26 Feb 2019 • Runsheng Zhang, Yaping Huang, Mengyang Pu, Jian Zhang, Qingji Guan, Qi Zou, Haibin Ling
To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs).