539 citations · 703 across the 32 of their papers we have counts for
35 papers
Improved Noise Schedule for Diffusion Training
Tiankai Hang, Shuyang Gu, Xin Geng +1
Diffusion models have emerged as the de facto choice for generating high-quality visual signals across various domains. However, training a single model to predict noise across var…
Can Class-Priors Help Single-Positive Multi-Label Learning?
Biao Liu, Ning Xu, Jie Wang +1
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label. Exi…
Robust Representation Learning for Unreliable Partial Label Learning
Yu Shi, Dong-Dong Wu, Xin Geng +1
Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. How…
Exploiting Multi-Label Correlation in Label Distribution Learning
Zhiqiang Kou jing wang yuheng jia xin geng
Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in…
Variational Label-Correlation Enhancement for Congestion Prediction
Biao Liu, Congyu Qiao, Ning Xu +3
The physical design process of large-scale designs is a time-consuming task, often requiring hours to days to complete, with routing being the most critical and complex step. As th…
NormAUG: Normalization-guided Augmentation for Domain Generalization
Lei Qi, Hongpeng Yang, Yinghuan Shi +1
Deep learning has made significant advancements in supervised learning. However, models trained in this setting often face challenges due to domain shift between training and test…
Generalizable Metric Network for Cross-domain Person Re-identification
Lei Qi, Ziang Liu, Yinghuan Shi +1
Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain genera…
Towards Effective Visual Representations for Partial-Label Learning
Shiyu Xia, Jiaqi Lv, Ning Xu +2
Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning…
Patch-aware Batch Normalization for Improving Cross-domain Robustness
Lei Qi, Dongjia Zhao, Yinghuan Shi +1
Despite the significant success of deep learning in computer vision tasks, cross-domain tasks still present a challenge in which the model's performance will degrade when the train…
Data Augmentation For Label Enhancement
Zhiqiang Kou, Yuheng Jia, Jing Wang +2
Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless…
Learning From Biased Soft Labels
Hua Yuan, Ning Xu, Yu Shi +2
Knowledge distillation has been widely adopted in a variety of tasks and has achieved remarkable successes. Since its inception, many researchers have been intrigued by the dark kn…
Inaccurate Label Distribution Learning
Zhiqiang Kou, Yuheng Jia, Jing Wang +1
Label distribution learning (LDL) trains a model to predict the relevance of a set of labels (called label distribution (LD)) to an instance. The previous LDL methods all assumed t…
Unreliable Partial Label Learning with Recursive Separation
Yu Shi, Ning Xu, Hua Yuan +1
Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. Howe…
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…
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Boyu Zhang, Jiayuan Chen, Yinfei Xu +3
The action quality assessment (AQA) of videos is a challenging vision task since the relation between videos and action scores is difficult to model. Thus, AQA has been widely stud…
Instance-Dependent Partial Label Learning
Ning Xu, Congyu Qiao, Xin Geng +1
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.…
Learngene: From Open-World to Your Learning Task
Qiufeng Wang, Xin Geng, Shuxia Lin +3
Although deep learning has made significant progress on fixed large-scale datasets, it typically encounters challenges regarding improperly detecting unknown/unseen classes in the…