Search Results for author: QinGhua Hu

Found 32 papers, 17 papers with code

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

Interactive Model with Structural Loss for Language-based Abductive Reasoning

no code implementations1 Dec 2021 Linhao Li, Ming Xu, Yongfeng Dong, Xin Li, Ao Wang, QinGhua Hu

Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called ``joint softmax focal loss'' in this paper.

Language Modelling Natural Language Inference

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

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.

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

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.

Imputation MULTI-VIEW LEARNING +1

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

7 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 +1

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 +1

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

1 code implementation5 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.

Object Counting Object Detection

Detection and Tracking Meet Drones Challenge

1 code implementation16 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 Video Object Detection

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.

MULTI-VIEW LEARNING

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 +3

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

2 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.

Single Image Deraining SSIM

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

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

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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