539 citations · 703 across the 32 of their papers we have counts for
8 papers · 1 filter
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
Lei Qi, Hongpeng Yang, Yinghuan Shi +1
Domain generalization (DG) aims at learning a model on source domains to well generalize on the unseen target domain. Although it has achieved great success, most of existing metho…
Progressive Purification for Instance-Dependent Partial Label Learning
Ning Xu, Biao Liu, Jiaqi Lv +2
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is corre…
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Ning Xu, Congyu Qiao, Jiaqi Lv +2
Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training exa…
Label Distribution Learning for Generalizable Multi-source Person Re-identification
Lei Qi, Jiaying Shen, Jiaqi Liu +2
Person re-identification (Re-ID) is a critical technique in the video surveillance system, which has achieved significant success in the supervised setting. However, it is difficul…
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Jin Yuan, Feng Hou, Yangzhou Du +4
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA)…
Decompositional Generation Process for Instance-Dependent Partial Label Learning
Congyu Qiao, Ning Xu, Xin Geng
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true.…
Graph Attention Transformer Network for Multi-Label Image Classification
Jin Yuan, Shikai Chen, Yao Zhang +4
Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize su…
Learning Hierarchical Graph Representation for Image Manipulation Detection
Wenyan Pan, Zhili Zhou, Miaogen Ling +2
The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural…