Search Results for author: Shiming Ge

Found 20 papers, 6 papers with code

Selective-Supervised Contrastive Learning with Noisy Labels

1 code implementation8 Mar 2022 Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu

In the selection process, by measuring the agreement between learned representations and given labels, we first identify confident examples that are exploited to build confident pairs.

Contrastive Learning Learning with noisy labels +1

Trustable Co-label Learning from Multiple Noisy Annotators

1 code implementation8 Mar 2022 Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge

This raises the following important question: how can we effectively use a small amount of trusted data to facilitate robust classifier learning from multiple annotators?

WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

1 code implementation19 Jan 2022 Chunhui Zhang, Guanjie Huang, Li Liu, Shan Huang, Yinan Yang, Yuxuan Zhang, Xiang Wan, Shiming Ge

In this work, we contribute a new million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark, called WebUAV-3M.


Robust Weight Perturbation for Adversarial Training

no code implementations29 Sep 2021 Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu

In this paper, we propose such a criterion, namely Loss Stationary Condition (LSC) for constrained perturbation.

The 2nd Anti-UAV Workshop & Challenge: Methods and Results

no code implementations23 Aug 2021 Jian Zhao, Gang Wang, Jianan Li, Lei Jin, Nana Fan, Min Wang, Xiaojuan Wang, Ting Yong, Yafeng Deng, Yandong Guo, Shiming Ge, Guodong Guo

The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.

Object Tracking

Interpretable Face Manipulation Detection via Feature Whitening

no code implementations21 Jun 2021 Yingying Hua, Daichi Zhang, Pengju Wang, Shiming Ge

The approach could make the face manipulation detection process transparent by embedding the feature whitening module.


Student Network Learning via Evolutionary Knowledge Distillation

no code implementations23 Mar 2021 Kangkai Zhang, Chunhui Zhang, Shikun Li, Dan Zeng, Shiming Ge

Inspired by that, we propose an evolutionary knowledge distillation approach to improve the transfer effectiveness of teacher knowledge.

Knowledge Distillation Transfer Learning

Receptive Multi-granularity Representation for Person Re-Identification

no code implementations31 Aug 2020 Guanshuo Wang, Yufeng Yuan, Jiwei Li, Shiming Ge, Xi Zhou

Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment.

Person Re-Identification

Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition

no code implementations24 Dec 2019 Jialin Gao, Tong He, Xi Zhou, Shiming Ge

A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints.

Action Recognition Frame +1

Aesthetic Attributes Assessment of Images

2 code implementations11 Jul 2019 Xin Jin, Le Wu, Geng Zhao, Xiao-Dong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou

This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.

Image Captioning Transfer Learning

Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency

no code implementations10 Apr 2019 Jia Li, Kui Fu, Shengwei Zhao, Shiming Ge

In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model.

Knowledge Distillation Saliency Prediction

Ultrafast Video Attention Prediction with Coupled Knowledge Distillation

no code implementations9 Apr 2019 Kui Fu, Peipei Shi, Yafei Song, Shiming Ge, Xiangju Lu, Jia Li

To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net.

Knowledge Distillation

Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation

no code implementations25 Nov 2018 Shiming Ge, Shengwei Zhao, Chenyu Li, Jia Li

In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively.

Face Model Face Recognition +1

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

2 code implementations23 Aug 2017 Xin Jin, Le Wu, Xiao-Dong Li, Siyu Chen, Siwei Peng, Jingying Chi, Shiming Ge, Chenggen Song, Geng Zhao

Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).

Single Reference Image based Scene Relighting via Material Guided Filtering

no code implementations23 Aug 2017 Xin Jin, Yannan Li, Ningning Liu, Xiao-Dong Li, Xianggang Jiang, Chaoen Xiao, Shiming Ge

We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer decomposition.

Image Relighting

Privacy Preserving Face Retrieval in the Cloud for Mobile Users

no code implementations9 Aug 2017 Xin Jin, Shiming Ge, Chenggen Song

The experimental results reveal that our protocol can successfully retrieve the proper photos from the cloud server and protect the user photos and the face detector.

Detecting Masked Faces in the Wild With LLE-CNNs

no code implementations CVPR 2017 Shiming Ge, Jia Li, Qiting Ye, Zhao Luo

Detecting masked faces (i. e., faces with occlusions) is a challenging task due to two main reasons: 1)the absence of large datasets of masked faces, and 2)the absence of facial cues from the masked regions.

ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation

2 code implementations7 Oct 2016 Xin Jin, Le Wu, Xiao-Dong Li, Xiaokun Zhang, Jingying Chi, Siwei Peng, Shiming Ge, Geng Zhao, Shuying Li

Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.

Domain Adaptation General Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.