2 citations · 3 across the 7 of their papers we have counts for
7 papers
DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction
Chaofan Gan, Yuanpeng Tu, Yuxi Li +1
With the recent burst of 2D and 3D data, cross-modal retrieval has attracted increasing attention recently. However, manual labeling by non-experts will inevitably introduce corrup…
Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery
Yuanpeng Tu, Zhun Zhong, Yuxi Li +1
Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning, where only part of the category labels are assigned to…
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification
Shuguang Dou, Xiangyang Jiang, Yuanpeng Tu +4
The paper introduces the Decouple Re-identificatiOn and human Parsing (DROP) method for occluded person re-identification (ReID). Unlike mainstream approaches using global features…
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
Yuanpeng Tu, Boshen Zhang, Liang Liu +6
Industrial anomaly detection is generally addressed as an unsupervised task that aims at locating defects with only normal training samples. Recently, numerous 2D anomaly detection…
Learning from Noisy Labels with Decoupled Meta Label Purifier
Yuanpeng Tu, Boshen Zhang, Yuxi Li +5
Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-lear…
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Yuanpeng Tu, Boshen Zhang, Yuxi Li +6
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithm…