Search Results for author: QinGhua Hu

Found 42 papers, 22 papers with code

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.


Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion

1 code implementation2 Feb 2023 Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu

The MoLE performs specialized learning of multi-modal local features, prompting the fused images to retain the local information in a sample-adaptive manner, while the MoGE focuses on the global information that complements the fused image with overall texture detail and contrast.

Infrared And Visible Image Fusion

Reliable and Interpretable Personalized Federated Learning

no code implementations CVPR 2023 Zixuan Qin, Liu Yang, Qilong Wang, Yahong Han, QinGhua Hu

When there are large differences in data distribution among clients, it is crucial for federated learning to design a reliable client selection strategy and an interpretable client communication framework to better utilize group knowledge.

Personalized Federated Learning

HS-Diffusion: Learning a Semantic-Mixing Diffusion Model for Head Swapping

no code implementations13 Dec 2022 Qinghe Wang, Lijie Liu, Miao Hua, Qian He, Pengfei Zhu, Bing Cao, QinGhua Hu

We blend the semantic layouts of source head and source body, and then inpaint the transition region by the semantic layout generator, achieving a coarse-grained head swapping.

DetFusion: A Detection-driven Infrared and Visible Image Fusion Network

1 code implementation ACMMM 2022 Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu

We cascade the image fusion network with the detection networks of both modalities and use the detection loss of the fused images to provide guidance on task-related information for the optimization of the image fusion network.

Infrared And Visible Image Fusion object-detection +1

Class-Specific Semantic Reconstruction for Open Set Recognition

no code implementations5 Jul 2022 Hongzhi Huang, Yu Wang, QinGhua Hu, Ming-Ming Cheng

In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning.

Open Set Learning

Multi-Granularity Regularized Re-Balancing for Class Incremental Learning

1 code implementation30 Jun 2022 Huitong Chen, Yu Wang, QinGhua Hu

Re-balancing methods are used to alleviate the influence of data imbalance; however, we empirically discover that they would under-fit new classes.

class-incremental learning Class Incremental Learning +1

Single Object Tracking Research: A Survey

no code implementations25 Apr 2022 Ruize Han, Wei Feng, Qing Guo, QinGhua Hu

Visual object tracking is an important task in computer vision, which has many real-world applications, e. g., video surveillance, visual navigation.

object-detection Video Object Detection +3

Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

1 code implementation19 Mar 2022 Junwen Pan, Pengfei Zhu, Kaihua Zhang, Bing Cao, Yu Wang, Dingwen Zhang, Junwei Han, QinGhua Hu

Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently.

Pseudo Label Semi-Supervised Semantic Segmentation +2

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

Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking

no code implementations23 Nov 2021 Pengfei Zhu, Hongtao Yu, Kaihua Zhang, Yu Wang, Shuai Zhao, Lei Wang, Tianzhu Zhang, QinGhua Hu

To address this issue, segmentation-based trackers have been proposed that employ per-pixel matching to improve the tracking performance of deformable objects effectively.

Visual Object Tracking Visual Tracking

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

Temporal-attentive Covariance Pooling Networks for Video Recognition

1 code implementation NeurIPS 2021 Zilin Gao, Qilong Wang, Bingbing Zhang, QinGhua Hu, Peihua Li

Then, a temporal covariance pooling performs temporal pooling of the attentive covariance representations to characterize both intra-frame correlations and inter-frame cross-correlations of the calibrated features.

Video Recognition

T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

1 code implementation ICCV 2021 Ruihuang Li, Xu Jia, Jianzhong He, Shuaijun Chen, QinGhua Hu

Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.

Unsupervised Domain Adaptation

Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark

1 code implementation CVPR 2021 Longyin Wen, Dawei Du, Pengfei Zhu, QinGhua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu

To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd, formed by 112 video clips with 33, 600 HD frames in various scenarios.

object-detection Object Detection

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

Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification

no code implementations13 Jul 2020 Yucan Zhou, Yu Wang, Jianfei Cai, Yu Zhou, QinGhua Hu, Weiping Wang

Some works in the optimization of deep neural networks have shown that a better arrangement of training data can make the classifier converge faster and perform better.

General Classification Meta-Learning

Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation

6 code implementations7 May 2020 Zhaohui Zheng, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, QinGhua Hu, WangMeng Zuo

In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.

Instance Segmentation object-detection +3

What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective

1 code implementation CVPR 2020 Qilong Wang, Li Zhang, Banggu Wu, Dongwei Ren, Peihua Li, WangMeng Zuo, QinGhua Hu

Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task.

Instance Segmentation object-detection +2

Multi-Drone based Single Object Tracking with Agent Sharing Network

1 code implementation16 Mar 2020 Pengfei Zhu, Jiayu Zheng, Dawei Du, Longyin Wen, Yiming Sun, QinGhua Hu

Moreover, an agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones, which can improve the tracking accuracy significantly compared with single drone tracking.

Object Tracking

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning

2 code implementations5 Mar 2020 Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu

To address this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to extract complementary information from cross-modal images, which can significantly improve the detection performance in low light conditions.

Management Object Counting +1

Detection and Tracking Meet Drones Challenge

2 code implementations16 Jan 2020 Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Heng Fan, QinGhua Hu, Haibin Ling

We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.

Multi-Object Tracking object-detection +1

Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

1 code implementation4 Dec 2019 Longyin Wen, Dawei Du, Pengfei Zhu, QinGhua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu

This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones with arbitrary crowd density, perspective, and flight altitude.

Crowd Counting

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.


ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

10 code implementations CVPR 2020 Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, WangMeng Zuo, QinGhua Hu

By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity.

Dimensionality Reduction Image Classification +4

Neural Blind Deconvolution Using Deep Priors

1 code implementation CVPR 2020 Dongwei Ren, Kai Zhang, Qilong Wang, QinGhua Hu, WangMeng Zuo

To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.

Deblurring Self-Supervised Learning

Progressive Image Deraining Networks: A Better and Simpler Baseline

4 code implementations CVPR 2019 Dongwei Ren, WangMeng Zuo, QinGhua Hu, Pengfei Zhu, Deyu Meng

To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.

Image Super-Resolution Single Image Deraining +1

Semi-interactive Attention Network for Answer Understanding in Reverse-QA

no code implementations12 Jan 2019 Qing Yin, Guan Luo, Xiaodong Zhu, QinGhua Hu, Ou wu

Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities.

Question Answering text-classification

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

SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection

no code implementations21 Sep 2018 Meijun Sun, Ziqi Zhou, QinGhua Hu, Zheng Wang, Jianmin Jiang

To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance.

Video Saliency Detection

Detecting Adversarial Examples via Key-based Network

no code implementations2 Jun 2018 Pinlong Zhao, Zhouyu Fu, Ou wu, QinGhua Hu, Jun Wang

In contrast to existing defense methods, the proposed method does not require knowledge of the process for generating adversarial examples and can be applied to defend against different types of attacks.

Vision Meets Drones: A Challenge

no code implementations20 Apr 2018 Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Ling, QinGhua Hu

In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform.

Multi-Object Tracking object-detection +1

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

Efficient Background Modeling Based on Sparse Representation and Outlier Iterative Removal

no code implementations23 Jan 2014 Linhao Li, Ping Wang, QinGhua Hu, Sijia Cai

A cyclic iteration process is then proposed to extract the background from the discriminative frame set.

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