Search Results for author: Changqing Zhang

Found 34 papers, 11 papers with code

Semantic Invariant Multi-view Clustering with Fully Incomplete Information

no code implementations22 May 2023 Pengxin Zeng, Mouxing Yang, Yiding Lu, Changqing Zhang, Peng Hu, Xi Peng

To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples.


Reweighted Mixup for Subpopulation Shift

no code implementations9 Apr 2023 Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, QinGhua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao

Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.

Fairness Generalization Bounds

Fairness-guided Few-shot Prompting for Large Language Models

2 code implementations23 Mar 2023 Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, QinGhua Hu, Bingzhe Wu

Large language models have demonstrated surprising ability to perform in-context learning, i. e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples.


Balanced Audiovisual Dataset for Imbalance Analysis

no code implementations14 Feb 2023 Wenke Xia, Xu Zhao, Xincheng Pang, Changqing Zhang, Di Hu

We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias.

Graph Matching with Bi-level Noisy Correspondence

no code implementations8 Dec 2022 Yijie Lin, Mouxing Yang, Jun Yu, Peng Hu, Changqing Zhang, Xi Peng

In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).

Contrastive Learning Graph Matching

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

1 code implementation19 Sep 2022 Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao

Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.

Generalization Bounds

Trusted Multi-View Classification with Dynamic Evidential Fusion

1 code implementation25 Apr 2022 Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou

With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.


Uncertainty-Aware Multi-View Representation Learning

no code implementations15 Jan 2022 Yu Geng, Zongbo Han, Changqing Zhang, QinGhua Hu

Under the help of uncertainty, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated.

MULTI-VIEW LEARNING Representation Learning

Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification

1 code implementation CVPR 2022 Zongbo Han, Fan Yang, Junzhou Huang, Changqing Zhang, Jianhua Yao

To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.

Informativeness Medical Diagnosis +1

Trustworthy Long-Tailed Classification

1 code implementation CVPR 2022 Bolian Li, Zongbo Han, Haining Li, Huazhu Fu, Changqing Zhang

To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework.

Classification Long-tail Learning +1

Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions

1 code implementation NeurIPS 2021 Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu

Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications.

Multimodal Sentiment Analysis regression

Identifying Incorrect Classifications with Balanced Uncertainty

1 code implementation15 Oct 2021 Bolian Li, Zige Zheng, Changqing Zhang

Uncertainty estimation is critical for cost-sensitive deep-learning applications (i. e. disease diagnosis).

Out of Distribution (OOD) Detection

Trusted Multi-View Classification

4 code implementations ICLR 2021 Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou

To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.

Classification General Classification +1

Multi-View Disentangled Representation

no code implementations1 Jan 2021 Zongbo Han, Changqing Zhang, Huazhu Fu, QinGhua Hu, Joey Tianyi Zhou

Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition.


Deep Partial Multi-View Learning

no code implementations12 Nov 2020 Changqing Zhang, Yajie Cui, Zongbo Han, Joey Tianyi Zhou, Huazhu Fu, QinGhua Hu

Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing.


SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning

no code implementations ECCV 2020 Junbing Li, Changqing Zhang, Pengfei Zhu, Baoyuan Wu, Lei Chen, QinGhua Hu

Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels.

General Classification Multi-Label Classification +1

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

no code implementations1 Jun 2020 Tao Zhou, Huazhu Fu, Yu Zhang, Changqing Zhang, Xiankai Lu, Jianbing Shen, Ling Shao

Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.

Ball k-means

no code implementations2 May 2020 Shuyin Xia, Daowan Peng, Deyu Meng, Changqing Zhang, Guoyin Wang, Zizhong Chen, Wei Wei

The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.

CPM-Nets: Cross Partial Multi-View Networks

1 code implementation NeurIPS 2019 Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu

Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.


AE2-Nets: Autoencoder in Autoencoder Networks

no code implementations CVPR 2019 Changqing Zhang, Yeqing Liu, Huazhu Fu

The proposed method has the following merits: (1) our model jointly performs view-specific representation learning (with the inner autoencoder networks) and multi-view information encoding (with the outer autoencoder networks) in a unified framework; (2) due to the degradation process from the latent representation to each single view, our model flexibly balances the complementarity and consistence among multiple views.

Representation Learning

Unsupervised Degradation Learning for Single Image Super-Resolution

no code implementations11 Dec 2018 Tianyu Zhao, Wenqi Ren, Changqing Zhang, Dongwei Ren, QinGhua Hu

Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle.

Image Super-Resolution

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

3 code implementations19 May 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.

K-means clustering for efficient and robust registration of multi-view point sets

no code implementations14 Oct 2017 Zutao Jiang, Jihua Zhu, Georgios D. Evangelidis, Changqing Zhang, Shanmin Pang, Yaochen Li

Subsequently, the shape comprised by all cluster centroids is used to sequentially estimate the rigid transformation for each point set.

Latent Multi-View Subspace Clustering

no code implementations CVPR 2017 Changqing Zhang, QinGhua Hu, Huazhu Fu, Pengfei Zhu, Xiaochun Cao

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views.

Multi-view Subspace Clustering

SketchNet: Sketch Classification With Web Images

no code implementations CVPR 2016 Hua Zhang, Si Liu, Changqing Zhang, Wenqi Ren, Rui Wang, Xiaochun Cao

In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images.

Classification General Classification

Low-Rank Tensor Constrained Multiview Subspace Clustering

no code implementations ICCV 2015 Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao

We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC).

Diversity-Induced Multi-View Subspace Clustering

no code implementations CVPR 2015 Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang

In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features.

Face Clustering Multi-view Subspace Clustering

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