research
∙
08/31/2023
Robust Representation Learning for Unreliable Partial Label Learning
Partial Label Learning (PLL) is a type of weakly supervised learning whe...
share
research
∙
08/02/2023
A Novel Cross-Perturbation for Single Domain Generalization
Single domain generalization aims to enhance the ability of the model to...
research
∙
08/01/2023
Variational Label-Correlation Enhancement for Congestion Prediction
The physical design process of large-scale designs is a time-consuming t...
research
∙
07/25/2023
NormAUG: Normalization-guided Augmentation for Domain Generalization
Deep learning has made significant advancements in supervised learning. ...
research
∙
06/17/2023
Genes in Intelligent Agents
Training intelligent agents in Reinforcement Learning (RL) is much more ...
research
∙
05/24/2023
Exploring Diverse In-Context Configurations for Image Captioning
After discovering that Language Models (LMs) can be good in-context few-...
research
∙
05/10/2023
Towards Effective Visual Representations for Partial-Label Learning
Under partial-label learning (PLL) where, for each training instance, on...
research
∙
05/03/2023
Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models
During the continuous evolution of one organism's ancestry, its genes ac...
research
∙
04/06/2023
Patch-aware Batch Normalization for Improving Cross-domain Robustness
Despite the significant success of deep learning in computer vision task...
research
∙
03/21/2023
Data Augmentation For Label Enhancement
Label distribution (LD) uses the description degree to describe instance...
research
∙
03/16/2023
Efficient Diffusion Training via Min-SNR Weighting Strategy
Denoising diffusion models have been a mainstream approach for image gen...
research
∙
02/25/2023
Inaccurate Label Distribution Learning
Label distribution learning (LDL) trains a model to predict the relevanc...
research
∙
02/20/2023
Unreliable Partial Label Learning with Recursive Separation
Partial label learning (PLL) is a typical weakly supervised learning pro...
research
∙
02/16/2023
Learning From Biased Soft Labels
Knowledge distillation has been widely adopted in a variety of tasks and...
research
∙
08/11/2022
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
Domain generalization (DG) aims at learning a model on source domains to...
research
∙
06/02/2022
Progressive Purification for Instance-Dependent Partial Label Learning
Partial label learning (PLL) aims to train multi-class classifiers from ...
research
∙
06/01/2022
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Multi-label learning (MLL) learns from the examples each associated with...
research
∙
04/12/2022
Label Distribution Learning for Generalizable Multi-source Person Re-identification
Person re-identification (Re-ID) is a critical technique in the video su...
research
∙
04/08/2022
Decomposition-based Generation Process for Instance-Dependent Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning pro...
research
∙
04/08/2022
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data ...
research
∙
03/08/2022
Graph Attention Transformer Network for Multi-Label Image Classification
Multi-label classification aims to recognize multiple objects or attribu...
research
∙
01/15/2022
Learning Hierarchical Graph Representation for Image Manipulation Detection
The objective of image manipulation detection is to identify and locate ...
research
∙
11/22/2021
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Action quality assessment (AQA) from videos is a challenging vision task...
research
∙
10/25/2021
Instance-Dependent Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning pro...
research
∙
06/12/2021
Learngene: From Open-World to Your Learning Task
Although deep learning has made significant progress on fixed large-scal...
research
∙
06/11/2021
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classificatio...
research
∙
09/18/2020
Compact Learning for Multi-Label Classification
Multi-label classification (MLC) studies the problem where each instance...
research
∙
07/17/2020
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, wh...
research
∙
07/03/2020
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
Facial attributes (e.g., age and attractiveness) estimation performance ...
research
∙
02/19/2020
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning is one of the important weakly supervised learnin...
research
∙
05/14/2019
A Context-and-Spatial Aware Network for Multi-Person Pose Estimation
Multi-person pose estimation is a fundamental yet challenging task in co...
research
∙
05/09/2019
Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
Multi-person pose estimation is an important but challenging problem in ...
research
∙
06/26/2017
Multi-Label Learning with Label Enhancement
Multi-label learning deals with training instances associated with multi...
research
∙
11/06/2016