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WaveSpect: A Hybrid Approach to Synthetic Audio Detection via Waveform and Spectrogram Analysis
Dong Chen;Fan Huang;Zhengxuan Song;Wei Zhu;Yin Yang;Kun Zeng
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
With the rapid advancement of synthetic speech technology, the challenges posed by audio deepfakes have become increasingly severe. Despite notable progress in synthetic speech detection, existing algorithms exhibit limited generalization to unknown attacks. To address these challenges, we propose WaveSpect, which combines waveform and spectrogram features to capture subtle artifacts typically ove...Show More
KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network
Shiying Lin;Rong Hu;Zuoyong Li;Qinghua Lin;Kun Zeng;Xiang Wu
In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixe...Show More
A Generic Multicorrespondence Matching Framework for Reference-Based Image Super-Resolution
Hong Zheng;Huabin Heng;Zhiqiang Yan;Kun Zeng;Jinsheng Fang;Baohua Qiang
Image super-resolution (SR) plays an important role in vision-based measurements, and reference-based image super-resolution (RefSR) is one such SR technique that enhances the resolution of a low-resolution (LR) image using an external high-resolution (HR) reference image. However, most existing RefSR methods search for the best-matching correspondence in the reference image, typically a single pa...Show More
ITSMatch: Improved Safe Semi-Supervised Text Classification Under Class Distribution Mismatch
Kun Zeng;Jinlin Li;Yibin Xu;Xinjing Zhang;Ge Lin;Nanli Zeng;Yingying Qu
Deep semi-supervised learning (SSL) brings deep learning from lab with expensive label data costs to real-world commercial application. Today, deep SSL is being universally applied in various artificial intelligence commercial technologies. However, there may be a distribution mismatch between labeled and unlabeled datasets in practical application, which is a key issue that degrades deep SSL perf...Show More
Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning
2023 13th International Conference on Information Technology in Medicine and Education (ITME)
Year: 2023 | Conference Paper |
The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve ...Show More
Densely Connected Transformer With Linear Self-Attention for Lightweight Image Super-Resolution
Image super-resolution (SR) is the process of restoring high-resolution (HR) images from low-resolution (LR) ones. Recent Transformer-based SR methods have achieved impressive results by utilizing the self-attention (SA) mechanism, which allows modeling long-range dependencies among input features in spatial dimensions. However, the computational complexity of SA increases quadratically with respe...Show More
A Network Combining CNN and Transformer for Blind Image Super-Resolution
2022 12th International Conference on Information Technology in Medicine and Education (ITME)
Year: 2022 | Conference Paper |
Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining ...Show More
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li;Kai Zhang;Radu Timofte;Luc Van Gool;Fangyuan Kong;Mingxi Li;Songwei Liu;Zongcai Du;Ding Liu;Chenhui Zhou;Jingyi Chen;Qingrui Han;Zheyuan Li;Yingqi Liu;Xiangyu Chen;Haoming Cai;Yu Qiao;Chao Dong;Long Sun;Jinshan Pan;Yi Zhu;Zhikai Zong;Xiaoxiao Liu;Zheng Hui;Tao Yang;Peiran Ren;Xuansong Xie;Xian-Sheng Hua;Yanbo Wang;Xiaozhong Ji;Chuming Lin;Donghao Luo;Ying Tai;Chengjie Wang;Zhizhong Zhang;Yuan Xie;Shen Cheng;Ziwei Luo;Lei Yu;Zhihong Wen;Qi Wul;Youwei Li;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Yuanfei Huang;Meiguang Jin;Hua Huang;Jing Liu;Xinjian Zhang;Yan Wang;Lingshun Long;Gen Li;Yuanfan Zhang;Zuowei Cao;Lei Sun;Panaetov Alexander;Yucong Wang;Minjie Cai;Li Wang;Lu Tian;Zheyuan Wang;Hongbing Ma;Jie Liu;Chao Chen;Yidong Cai;Jie Tang;Gangshan Wu;Weiran Wang;Shirui Huang;Honglei Lu;Huan Liu;Keyan Wang;Jun Chen;Shi Chen;Yuchun Miao;Zimo Huang;Lefei Zhang;Mustafa Ayazoğlu;Wei Xiong;Chengyi Xiong;Fei Wang;Hao Li;Ruimian Wen;Zhijing Yang;Wenbin Zou;Weixin Zheng;Tian Ye;Yuncheng Zhang;Xiangzhen Kong;Aditya Arora;Syed Waqas Zamir;Salman Khan;Munawar Hayat;Fahad Shahbaz Khan;Dandan Gao;Dengwen Zhou;Qian Ning;Jingzhu Tang;Han Huang;Yufei Wang;Zhangheng Peng;Haobo Li;Wenxue Guan;Shenghua Gong;Xin Li;Jun Liu;Wanjun Wang;Dengwen Zhou;Kun Zeng;Hanjiang Lin;Xinyu Chen;Jinsheng Fang
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (44)
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficien...Show More
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li;Kai Zhang;Radu Timofte;Luc Van Gool;Fangyuan Kong;Mingxi Li;Songwei Liu;Zongcai Du;Ding Liu;Chenhui Zhou;Jingyi Chen;Qingrui Han;Zheyuan Li;Yingqi Liu;Xiangyu Chen;Haoming Cai;Yu Qiao;Chao Dong;Long Sun;Jinshan Pan;Yi Zhu;Zhikai Zong;Xiaoxiao Liu;Zheng Hui;Tao Yang;Peiran Ren;Xuansong Xie;Xian-Sheng Hua;Yanbo Wang;Xiaozhong Ji;Chuming Lin;Donghao Luo;Ying Tai;Chengjie Wang;Zhizhong Zhang;Yuan Xie;Shen Cheng;Ziwei Luo;Lei Yu;Zhihong Wen;Qi Wul;Youwei Li;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Yuanfei Huang;Meiguang Jin;Hua Huang;Jing Liu;Xinjian Zhang;Yan Wang;Lingshun Long;Gen Li;Yuanfan Zhang;Zuowei Cao;Lei Sun;Panaetov Alexander;Yucong Wang;Minjie Cai;Li Wang;Lu Tian;Zheyuan Wang;Hongbing Ma;Jie Liu;Chao Chen;Yidong Cai;Jie Tang;Gangshan Wu;Weiran Wang;Shirui Huang;Honglei Lu;Huan Liu;Keyan Wang;Jun Chen;Shi Chen;Yuchun Miao;Zimo Huang;Lefei Zhang;Mustafa Ayazoğlu;Wei Xiong;Chengyi Xiong;Fei Wang;Hao Li;Ruimian Wen;Zhijing Yang;Wenbin Zou;Weixin Zheng;Tian Ye;Yuncheng Zhang;Xiangzhen Kong;Aditya Arora;Syed Waqas Zamir;Salman Khan;Munawar Hayat;Fahad Shahbaz Khan;Dandan Gao;Dengwen Zhou;Qian Ning;Jingzhu Tang;Han Huang;Yufei Wang;Zhangheng Peng;Haobo Li;Wenxue Guan;Shenghua Gong;Xin Li;Jun Liu;Wanjun Wang;Dengwen Zhou;Kun Zeng;Hanjiang Lin;Xinyu Chen;Jinsheng Fang
Year: 2022 | Conference Paper |
A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (86)
Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational complexity and heavy memory consumption, which is inappropriate to be applied on embedded terminals such as mobile platforms. In order to solve this problem, we propose a hybrid network ...Show More
Structured Attention Network for Referring Image Segmentation
Liang Lin;Pengxiang Yan;Xiaoqian Xu;Sibei Yang;Kun Zeng;Guanbin Li
Referring image segmentation aims at segmenting out the object or stuff referred to by a natural language expression. The challenge of this task lies in the requirement of understanding both vision and language. The linguistic structure of a referring expression can provide an intuitive and explainable layout for reasoning over visual and linguistic concepts. In this paper, we propose a structured...Show More
Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting
Feature matching is a fundamental problem in feature-based remote sensing image registration. Due to the ground relief variations and imaging viewpoint changes, remote sensing images often involve local distortions, leading to difficulties in high-accuracy image registration. To address this issue, in this article, we propose a robust feature matching method called First Neighbor Relation Guided (...Show More
Hypersonic Vehicle Trajectory Classification Using Improved CNN-LSTM Model
2021 IEEE International Conference on Unmanned Systems (ICUS)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
Hypersonic vehicle has become a hotspot of aircraft research because of its high speed and complex maneuvering mode. The classification of the trajectory of hypersonic vehicle is of great significance to the trajectory prediction and interception of hypersonic vehicles. This paper proposes a neural network structure combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (L...Show More
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Andrey Ignatov;Radu Timofte;Maurizio Denna;Abdel Younes;Andrew Lek;Mustafa Ayazoglu;Jie Liu;Zongcai Du;Jiaming Guo;Xueyi Zhou;Hao Jia;Youliang Yan;Zexin Zhang;Yixin Chen;Yunbo Peng;Yue Lin;Xindong Zhang;Hui Zeng;Kun Zeng;Peirong Li;Zhihuang Liu;Shiqi Xue;Shengpeng Wang
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (45)
Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first M...Show More
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Andrey Ignatov;Radu Timofte;Maurizio Denna;Abdel Younes;Andrew Lek;Mustafa Ayazoglu;Jie Liu;Zongcai Du;Jiaming Guo;Xueyi Zhou;Hao Jia;Youliang Yan;Zexin Zhang;Yixin Chen;Yunbo Peng;Yue Lin;Xindong Zhang;Hui Zeng;Kun Zeng;Peirong Li;Zhihuang Liu;Shiqi Xue;Shengpeng Wang
Year: 2021 | Conference Paper |
Occlusion-Guided Vertical Retargeting For Stereoscopic Images Based On Pixel Fusion
2020 IEEE International Conference on Image Processing (ICIP)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
To implement vertical retargeting for stereoscopic images, this paper proposes an occlusion-guided stereoscopic image retargeting method via the pixel fusion technique. Traditional seam searching-based methods cannot construct valid horizontal seam pairs due to the existence of occluded regions in stereoscopic images and thus fail to implement vertical retargeting. To solve this issue, we propose ...Show More
A Convolutional Attention Residual Network for Stereo Matching
Guangyi Huang;Yongyi Gong;Qingzhen Xu;Kanoksak Wattanachote;Kun Zeng;Xiaonan Luo
Deep learning based on convolutional neural network (CNN) has been successfully applied to stereo matching, which has achieved greater improvement in speed and accuracy compared with traditional methods. However, existing CNN-based stereo matching frameworks frequently encounter two problems. First, the existing stereo matching network has a large number of parameters, which results in too long ma...Show More
Sketch-Based Shape Retrieval via Best View Selection and a Cross-Domain Similarity Measure
Yongzhe Xu;Jiangchuan Hu;Kanoksak Wattanachote;Kun Zeng;YongYi Gong
IEEE Transactions on Multimedia
Cited by: Papers (21)
Retrieving 3D shapes from 2D human sketches has received increasing attention in computer vision and computer graphics. Most previous methods projected 3D shapes from numerous viewpoints and then extracted features of 3D shapes from these projections and calculated the similarity with sketches. However, due to the unknown pose of 3D shapes, viewpoints were usually sampled uniformly from a sphere c...Show More
Nowadays, the burgeon of machine learning has promoted its wide application in various fields. In Bioinformatics, machine learning computational method has become an indispensable part. Its efficiency and simplicity break the expensive and inefficient barriers of previous experimental methods. This article briefly reviews the application and development of machine learning in identifying and predi...Show More
Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentatio...Show More
Convolutional neural network (CNN) has achieved great success in the compressed sensing-based magnetic resonance imaging (CS-MRI). Latest deep networks for CS-MRI usually consist of a stack of sub-networks, each of which refines the former image prediction to a more accurate one. However, as the sub-network number increases, the information in prior sub-networks has a little influence on subsequen...Show More
We present a novel algorithm (DAM) for deterministic motion trajectory segmentation by using epipolar geometry and adaptive kernel-scale voting. DAM is based on geometric models and exploits the information derived from superpixels to deterministically construct a set of initial correlation matrices. Then DAM introduces a novel adaptive kernel-scale voting scheme to measure each initial correlatio...Show More
Sketch-Based Shape Retrieval via Multi-view Attention and Generalized Similarity
2018 7th International Conference on Digital Home (ICDH)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
Sketch-based shape retrieval has received increasing attention in computer vision and computer graphics. It suffers from the challenge gap between 2D sketches and 3D shapes. In this paper, we propose a generalized similarity matching framework based on a multi-view attention network (MVAN), which can retrieve 3D shape that is most similar to the query sketch. In proposed approach, firstly we compu...Show More
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (183)
This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were lear...Show More
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu
Year: 2018 | Conference Paper |
Persistent Memory Residual Network for Single Image Super Resolution
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (13)
Progresses has been witnessed in single image superresolution in which the low-resolution images are simulated by bicubic downsampling. However, for the complex image degradation in the wild such as downsampling, blurring, noises, and geometric deformation, the existing superresolution methods do not work well. Inspired by a persistent memory network which has been proven to be effective in image ...Show More
Single Image Super-Resolution With Learning Iteratively Non-Linear Mapping Between Low- and High-Resolution Sparse Representations
Kun Zeng;Hong Zheng;Yanyun Qu;Xiaobo Qu;Lijun Bao;Zhong Chen
2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Conventional sparse coding based super-resolution (SR) methods obtained promising performance by learning overcomplete dictionaries for low-resolution (LR) and high-resolution (HR) feature spaces, and assuming that the sparse representation of a HR feature vector was identical or linear to the sparse representation of the corresponding LR one. However, in fact, the relationship between LR and HR s...Show More
Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement
Hong Zheng;Kun Zeng;Di Guo;Jiaxi Ying;Yu Yang;Xi Peng;Feng Huang;Zhong Chen;Xiaobo Qu
In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of g...Show More
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