Search Results for author: Shuigeng Zhou

Found 26 papers, 8 papers with code

C3-STISR: Scene Text Image Super-resolution with Triple Clues

1 code implementation29 Apr 2022 Minyi Zhao, Miao Wang, Fan Bai, Bingjia Li, Jie Wang, Shuigeng Zhou

In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.

Image Super-Resolution Language Modelling

EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification

1 code implementation24 Apr 2022 Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, Shuigeng Zhou

However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully mine the potential of DA for NLP.

Data Augmentation Text Classification

An Empirical Study and Comparison of Recent Few-Shot Object Detection Algorithms

no code implementations27 Mar 2022 Tianying Liu, Lu Zhang, Yang Wang, Jihong Guan, Yanwei Fu, Shuigeng Zhou

To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learnt generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes.

Few-Shot Object Detection Meta-Learning +1

Fairness Amidst Non-IID Graph Data: A Literature Review

no code implementations15 Feb 2022 Wenbin Zhang, Jeremy C. Weiss, Shuigeng Zhou, Toby Walsh

Fairness in machine learning (ML), the process to understand and correct algorithmic bias, has gained increasing attention with numerous literature being carried out, commonly assume the underlying data is independent and identically distributed (IID).

Fairness

ARIBA: Towards Accurate and Robust Identification of Backdoor Attacks in Federated Learning

no code implementations9 Feb 2022 Yuxi Mi, Jihong Guan, Shuigeng Zhou

The distributed nature and privacy-preserving characteristics of federated learning make it prone to the threat of poisoning attacks, especially backdoor attacks, where the adversary implants backdoors to misguide the model on certain attacker-chosen sub-tasks.

Federated Learning Unsupervised Anomaly Detection

DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples

no code implementations NeurIPS 2021 Yi Xu, Jiandong Ding, Lu Zhang, Shuigeng Zhou

Extensive experiments on four standard SSL benchmarks show that DP-SSL can provide reliable labels for unlabeled data and achieve better classification performance on test sets than existing SSL methods, especially when only a small number of labeled samples are available.

Multiple-choice Semi-Supervised Image Classification

Weakly-supervised Text Classification Based on Keyword Graph

1 code implementation EMNLP 2021 Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, Shuigeng Zhou

Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts.

Classification Text Classification

Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction

no code implementations4 Aug 2021 Minyi Zhao, Yi Xu, Shuigeng Zhou

A number of deep learning based algorithms have been proposed to recover high-quality videos from low-quality compressed ones.

Frame Video Compression

ICDAR 2021 Competition on Scene Video Text Spotting

no code implementations26 Jul 2021 Zhanzhan Cheng, Jing Lu, Baorui Zou, Shuigeng Zhou, Fei Wu

During the competition period (opened on 1st March, 2021 and closed on 11th April, 2021), a total of 24 teams participated in the three proposed tasks with 46 valid submissions, respectively.

Text Spotting

Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss

no code implementations CVPR 2021 Lu Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang

Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set.

Few-Shot Object Detection

Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

no code implementations31 May 2021 Yi Xu, Minyi Zhao, Jing Liu, Xinjian Zhang, Longwen Gao, Shuigeng Zhou, Huyang Sun

Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos.

Frame Video Compression

Federated Face Recognition

no code implementations6 May 2021 Fan Bai, Jiaxiang Wu, Pengcheng Shen, Shaoxin Li, Shuigeng Zhou

Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.

Face Recognition Federated Learning

Text Recognition in Real Scenarios with a Few Labeled Samples

no code implementations22 Jun 2020 Jinghuang Lin, Zhanzhan Cheng, Fan Bai, Yi Niu, ShiLiang Pu, Shuigeng Zhou

Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications.

Domain Adaptation Scene Text Recognition

Object-QA: Towards High Reliable Object Quality Assessment

no code implementations27 May 2020 Jing Lu, Baorui Zou, Zhanzhan Cheng, ShiLiang Pu, Shuigeng Zhou, Yi Niu, Fei Wu

In this paper, we define the problem of object quality assessment for the first time and propose an effective approach named Object-QA to assess high-reliable quality scores for object images.

Object Recognition

Non-Local ConvLSTM for Video Compression Artifact Reduction

no code implementations ICCV 2019 Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun

Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos.

Frame Video Compression

Learning Competitive and Discriminative Reconstructions for Anomaly Detection

no code implementations17 Mar 2019 Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan

Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.

Anomaly Detection

You Only Recognize Once: Towards Fast Video Text Spotting

1 code implementation8 Mar 2019 Zhanzhan Cheng, Jing Lu, Yi Niu, ShiLiang Pu, Fei Wu, Shuigeng Zhou

Video text spotting is still an important research topic due to its various real-applications.

Frame Text Spotting

Global Semantic Consistency for Zero-Shot Learning

no code implementations22 Jun 2018 Fan Wu, Kai Tian, Jihong Guan, Shuigeng Zhou

In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning.

Generalized Zero-Shot Learning

Edit Probability for Scene Text Recognition

no code implementations CVPR 2018 Fan Bai, Zhanzhan Cheng, Yi Niu, ShiLiang Pu, Shuigeng Zhou

The advantage lies in that the training process can focus on the missing, superfluous and unrecognized characters, and thus the impact of the misalignment problem can be alleviated or even overcome.

Frame Scene Text Recognition

AON: Towards Arbitrarily-Oriented Text Recognition

1 code implementation CVPR 2018 Zhanzhan Cheng, Yangliu Xu, Fan Bai, Yi Niu, ShiLiang Pu, Shuigeng Zhou

Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts.

Optical Character Recognition Scene Text Recognition

Focusing Attention: Towards Accurate Text Recognition in Natural Images

no code implementations ICCV 2017 Zhanzhan Cheng, Fan Bai, Yunlu Xu, Gang Zheng, ShiLiang Pu, Shuigeng Zhou

FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images.

Scene Text Recognition

Label Propagation on K-partite Graphs with Heterophily

no code implementations21 Jan 2017 Dingxiong Deng, Fan Bai, Yiqi Tang, Shuigeng Zhou, Cyrus Shahabi, Linhong Zhu

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.

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