1 code implementation • 4 Mar 2024 • Chunwei Tian, Menghua Zheng, Bo Li, Yanning Zhang, Shichao Zhang, David Zhang
Specifically, mentioned paired watermark images are obtained in a self supervised way, and paired noisy images (i. e., noisy and reference images) are obtained in a supervised way.
no code implementations • 7 Feb 2024 • Natasha Butt, Blazej Manczak, Auke Wiggers, Corrado Rainone, David Zhang, Michaël Defferrard, Taco Cohen
Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay.
no code implementations • 29 Nov 2023 • Farbin Fayza, Cansu Demirkiran, Hanning Chen, Che-Kai Liu, Avi Mohan, Hamza Errahmouni, Sanggeon Yun, Mohsen Imani, David Zhang, Darius Bunandar, Ajay Joshi
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN).
no code implementations • 3 Aug 2023 • Zachary A. Daniels, Jun Hu, Michael Lomnitz, Phil Miller, Aswin Raghavan, Joe Zhang, Michael Piacentino, David Zhang
This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
no code implementations • 22 May 2023 • Zhuangqun Huang, Gil Keren, Ziran Jiang, Shashank Jain, David Goss-Grubbs, Nelson Cheng, Farnaz Abtahi, Duc Le, David Zhang, Antony D'Avirro, Ethan Campbell-Taylor, Jessie Salas, Irina-Elena Veliche, Xi Chen
In this work, we explore text augmentation for ASR using large-scale pre-trained neural networks, and systematically compare those to traditional text augmentation methods.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
1 code implementation • Neural Computing and Applications 2023 • Le Zhang, Yao Lu, Jinxing Li, Fanglin Chen, Guangming Lu, David Zhang
Image hiding secures information security in multimedia communication.
no code implementations • International Journal of Computer Vision 2022 • Zhenwei He, Lei Zhang, Xinbo Gao, David Zhang
Our proposed MAF has two distinct contributions: (1) The Hierarchical Domain Feature Alignment (HDFA) module is introduced to minimize the image-level domain disparity, where Scale Reduction Module (SRM) reduces the feature map size without information loss and increases the training efficiency.
no code implementations • 30 Nov 2022 • David Zhang, Micah Carroll, Andreea Bobu, Anca Dragan
One of the most successful paradigms for reward learning uses human feedback in the form of comparisons.
no code implementations • 10 Nov 2022 • Andros Tjandra, Nayan Singhal, David Zhang, Ozlem Kalinli, Abdelrahman Mohamed, Duc Le, Michael L. Seltzer
Later, we use our optimal tokenization strategy to train multiple embedding and output model to further improve our result.
1 code implementation • 26 Sep 2022 • Chunwei Tian, Yanning Zhang, WangMeng Zuo, Chia-Wen Lin, David Zhang, Yixuan Yuan
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
1 code implementation • 26 Sep 2022 • Chunwei Tian, Menghua Zheng, WangMeng Zuo, Bob Zhang, Yanning Zhang, David Zhang
In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i. e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB).
no code implementations • 13 Sep 2022 • Mu Yang, Andros Tjandra, Chunxi Liu, David Zhang, Duc Le, Ozlem Kalinli
Neural network pruning compresses automatic speech recognition (ASR) models effectively.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 17 Aug 2022 • Michael Lomnitz, Zachary Daniels, David Zhang, Michael Piacentino
To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD).
1 code implementation • CVPR 2022 • Xinyu Lin, Jinxing Li, Zeyu Ma, Huafeng Li, Shuang Li, Kaixiong Xu, Guangming Lu, David Zhang
Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID.
no code implementations • 10 Jun 2022 • Saurabh Farkya, Zachary Daniels, Aswin Nadamuni Raghavan, David Zhang, Michael Piacentino
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems.
no code implementations • 10 Jun 2022 • Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels, Gooitzen van der Wal, Aswin Raghavan, Yuzheng Zhang, Jun Hu, Michael Lomnitz, Michael Isnardi, David Zhang, Michael Piacentino
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators.
1 code implementation • 29 May 2022 • Chunwei Tian, Yixuan Yuan, Shichao Zhang, Chia-Wen Lin, WangMeng Zuo, David Zhang
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR).
no code implementations • 19 Mar 2022 • Qing Cai, Yiming Qian, Jinxing Li, Jun Lv, Yee-Hong Yang, Feng Wu, David Zhang
Transformer-based architectures start to emerge in single image super resolution (SISR) and have achieved promising performance.
1 code implementation • 25 Dec 2021 • Mu Li, Kede Ma, Jinxing Li, David Zhang
We first describe parametric pseudocylindrical representation as a generalization of common pseudocylindrical map projections.
no code implementations • 17 Nov 2021 • Zebin Lin, Wenjie Pei, Fanglin Chen, David Zhang, Guangming Lu
Instead of learning each of these diverse pedestrian appearance features individually as most existing methods do, we propose to perform contrastive learning to guide the feature learning in such a way that the semantic distance between pedestrians with different appearances in the learned feature space is minimized to eliminate the appearance diversities, whilst the distance between pedestrians and background is maximized.
Ranked #1 on Pedestrian Detection on TJU-Ped-campus
2 code implementations • 15 Oct 2021 • Chun-Mei Feng, Huazhu Fu, Tianfei Zhou, Yong Xu, Ling Shao, David Zhang
Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts.
no code implementations • 10 Oct 2021 • Zengwei Yao, Wenjie Pei, Fanglin Chen, Guangming Lu, David Zhang
Existing methods for speech separation either transform the speech signals into frequency domain to perform separation or seek to learn a separable embedding space by constructing a latent domain based on convolutional filters.
Ranked #7 on Speech Separation on WHAMR!
1 code implementation • 4 Oct 2021 • Zhaoqun Li, Xu Liang, Dandan Fan, Jinxing Li, David Zhang
Bimodal palmprint recognition leverages palmprint and palm vein images simultaneously, which achieves high accuracy by multi-model information fusion and has strong anti-falsification property.
no code implementations • 1 Oct 2021 • Xin Feng, Wenjie Pei, Fengjun Li, Fanglin Chen, David Zhang, Guangming Lu
Most existing methods for image inpainting focus on learning the intra-image priors from the known regions of the current input image to infer the content of the corrupted regions in the same image.
no code implementations • 9 Jul 2021 • Xiaohui Zhang, Vimal Manohar, David Zhang, Frank Zhang, Yangyang Shi, Nayan Singhal, Julian Chan, Fuchun Peng, Yatharth Saraf, Mike Seltzer
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 26 Jun 2021 • Huafeng Li, Kaixiong Xu, Jinxing Li, Guangming Lu, Yong Xu, Zhengtao Yu, David Zhang
Since human-labeled samples are free for the target set, unsupervised person re-identification (Re-ID) has attracted much attention in recent years, by additionally exploiting the source set.
1 code implementation • 25 Mar 2021 • Chunwei Tian, Yong Xu, WangMeng Zuo, Chia-Wen Lin, David Zhang
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution.
no code implementations • 3 Mar 2021 • Zhaoqun Li, Xu Liang, Dandan Fan, Jinxing Li, Wei Jia, David Zhang
To our best knowledge, it is the largest contactless palmprint image benchmark ever collected with regard to the number of individuals and palms.
1 code implementation • 9 Oct 2020 • Xin Feng, Wenjie Pei, Zihui Jia, Fanglin Chen, David Zhang, Guangming Lu
In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.
1 code implementation • 8 Jul 2020 • Chunwei Tian, Yong Xu, WangMeng Zuo, Bo Du, Chia-Wen Lin, David Zhang
The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.
2 code implementations • 20 Jun 2020 • Jui-Ting Huang, ASHISH SHARMA, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, Linjun Yang
In this paper, we discuss the techniques for applying EBR to a Facebook Search system.
no code implementations • 10 May 2020 • Mu Li, Kai Zhang, WangMeng Zuo, Radu Timofte, David Zhang
To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context.
1 code implementation • 30 Nov 2019 • Shervin Minaee, Amirali Abdolrashidi, Hang Su, Mohammed Bennamoun, David Zhang
Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years.
no code implementations • 7 Oct 2019 • Prateeth Nayak, David Zhang, Sek Chai
Quantization for deep neural networks have afforded models for edge devices that use less on-board memory and enable efficient low-power inference.
no code implementations • 20 Aug 2019 • Jun Xu, Zhou Xu, Wangpeng An, Haoqian Wang, David Zhang
In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification.
2 code implementations • 24 Jun 2019 • Mu Li, Kede Ma, Jane You, David Zhang, WangMeng Zuo
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
1 code implementation • 16 May 2019 • Feng Li, Xiaohe Wu, WangMeng Zuo, David Zhang, Lei Zhang
Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization.
1 code implementation • 1 Apr 2019 • Mu Li, WangMeng Zuo, Shuhang Gu, Jane You, David Zhang
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance.
1 code implementation • 25 Mar 2019 • Lei Zhang, Shan-Shan Wang, Guang-Bin Huang, WangMeng Zuo, Jian Yang, David Zhang
The merits of the proposed MCTL are four-fold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and domain adaptation is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric (GGDM) is presented, such that both the global and local discrepancy can be effectively and positively reduced; 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario.
3 code implementations • 26 Jul 2018 • Jun Xu, Mengyang Yu, Ling Shao, WangMeng Zuo, Deyu Meng, Lei Zhang, David Zhang
However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations.
no code implementations • ECCV 2018 • Jun Xu, Lei Zhang, David Zhang
Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN).
Ranked #3 on Denoising on Darmstadt Noise Dataset
no code implementations • 12 Jun 2018 • Jun Xu, Wangpeng An, Lei Zhang, David Zhang
The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization.
no code implementations • CVPR 2018 • Zhetong Liang, Jun Xu, David Zhang, Zisheng Cao, Lei Zhang
State-of-the-art tone mapping algorithms mostly decompose an image into a base layer and a detail layer, and process them accordingly.
1 code implementation • 12 Apr 2018 • Dongwei Ren, WangMeng Zuo, David Zhang, Lei Zhang, Ming-Hsuan Yang
For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.
2 code implementations • 7 Apr 2018 • Jun Xu, Hui Li, Zhetong Liang, David Zhang, Lei Zhang
In order to promote the study on this problem while implementing the concurrent real-world image denoising datasets, we construct a new benchmark dataset which contains comprehensive real-world noisy images of different natural scenes.
1 code implementation • 25 Jan 2018 • Jinxing Li, Bob Zhang, Guangming Lu, David Zhang
The deep hash functions are then learned through two networks by minimizing the gap between the learned features and discrete codes.
no code implementations • 15 Jan 2018 • Mu Li, Shuhang Gu, David Zhang, WangMeng Zuo
One key issue of arithmetic encoding method is to predict the probability of the current coding symbol from its context, i. e., the preceding encoded symbols, which usually can be executed by building a look-up table (LUT).
1 code implementation • 5 Oct 2017 • Feng Li, Yingjie Yao, Peihua Li, David Zhang, WangMeng Zuo, Ming-Hsuan Yang
The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance.
no code implementations • ICCV 2017 • Jun Xu, Lei Zhang, David Zhang, Xiangchu Feng
Most of the existing denoising algorithms are developed for grayscale images, while it is not a trivial work to extend them for color image denoising because the noise statistics in R, G, B channels can be very different for real noisy images.
Ranked #5 on Denoising on Darmstadt Noise Dataset
no code implementations • 12 May 2017 • Jun Xu, Lei Zhang, David Zhang
We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising.
1 code implementation • CVPR 2018 • Mu Li, WangMeng Zuo, Shuhang Gu, Debin Zhao, David Zhang
Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database.
no code implementations • 27 Mar 2017 • Aswin Raghavan, Mohamed Amer, Timothy Shields, David Zhang, Sek Chai
GPU activity prediction is an important and complex problem.
no code implementations • 24 Mar 2017 • Sek Chai, Aswin Raghavan, David Zhang, Mohamed Amer, Tim Shields
In this paper, we present a unique approach using lower precision weights for more efficient and faster training phase.
no code implementations • 18 Oct 2016 • Mu Li, WangMeng Zuo, David Zhang
In general, our model consists of a mask network and an attribute transform network which work in synergy to generate a photo-realistic facial image with the reference attribute.
Ranked #2 on Image-to-Image Translation on RaFD
no code implementations • 23 Aug 2016 • Mu Li, WangMeng Zuo, David Zhang
Here we address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image.
no code implementations • CVPR 2016 • Faqiang Wang, WangMeng Zuo, Liang Lin, David Zhang, Lei Zhang
Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR).
no code implementations • 12 Apr 2016 • Jinxing Li, David Zhang, Yongcheng Li, Jian Wu
has proved that tongue, face and sublingual diagnosis as a noninvasive method is a reasonable way for disease detection.
no code implementations • 25 Mar 2016 • Yan Yan, Hanzi Wang, Si Chen, Xiaochun Cao, David Zhang
This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes.
2 code implementations • 15 Mar 2016 • Ke Yan, Lu Kou, David Zhang
In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space.
no code implementations • 23 Feb 2016 • Zheng Zhang, Yong Xu, Jian Yang, Xuelong. Li, David Zhang
The main purpose of this article is to provide a comprehensive study and an updated review on sparse representation and to supply a guidance for researchers.
no code implementations • 29 Dec 2015 • Da-Han Wang, Hanzi Wang, Dong Zhang, Jonathan Li, David Zhang
For character detection, we use the HSC features instead of using the Histograms of Oriented Gradients (HOG) features.
no code implementations • ICCV 2015 • Jun Xu, Lei Zhang, WangMeng Zuo, David Zhang, Xiangchu Feng
PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior.
no code implementations • 8 Jun 2015 • Lei Zhang, David Zhang
In this report, we have discussed the nearest neighbor, support vector machines and extreme learning machines for image classification under deep convolutional activation feature representation.
no code implementations • 24 May 2015 • Lei Zhang, David Zhang
This paper proposes a unified framework, referred to as Domain Adaptation Extreme Learning Machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gases recognition in E-nose systems, without loss of the computational efficiency and learning ability of traditional ELM.
no code implementations • 20 May 2015 • Lei Zhang, David Zhang
This paper studies visual understanding via a newly proposed l_2-norm based multi-feature shared learning framework, which can simultaneously learn a global label matrix and multiple sub-classifiers with the labeled multi-feature data.
no code implementations • 17 May 2015 • Lei Zhang, David Zhang
Conventional extreme learning machines solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification.
no code implementations • 17 May 2015 • Lei Zhang, David Zhang
It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the l_(2, 1)-norm of the network output weights and the learning error simultaneously.
no code implementations • 2 Feb 2015 • Wangmeng Zuo, Faqiang Wang, David Zhang, Liang Lin, Yuchi Huang, Deyu Meng, Lei Zhang
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification.
no code implementations • 8 Nov 2013 • Kaihua Zhang, Lei Zhang, Ming-Hsuan Yang, David Zhang
In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking.
no code implementations • 23 Sep 2013 • Faqiang Wang, WangMeng Zuo, Lei Zhang, Deyu Meng, David Zhang
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade.
no code implementations • 30 Aug 2013 • Pengfei Zhu, WangMeng Zuo, Lei Zhang, Simon C. K. Shiu, David Zhang
One key issue of ISFR is how to effectively and efficiently represent the query face image set by using the gallery face image sets.
no code implementations • CVPR 2013 • Wangmeng Zuo, Lei Zhang, Chunwei Song, David Zhang
Image denoising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods.
no code implementations • 30 May 2013 • Kaihua Zhang, Lei Zhang, Kin-Man Lam, David Zhang
The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window.
no code implementations • 11 Apr 2012 • Lei Zhang, Meng Yang, Xiangchu Feng, Yi Ma, David Zhang
It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored.
no code implementations • 20 Feb 2012 • Meng Yang, Lei Zhang, Jian Yang, David Zhang
Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR).
no code implementations • IEEE Transactions on Image Processing 2011 • Lin Zhang, Lei Zhang, Xuanqin Mou, David Zhang
Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations.
Ranked #2 on Image Quality Assessment on MSU FR VQA Database
no code implementations • Pattern Recognition 2003 • Jian Yang; Zhong Jin; Jing-yu Yang, David Zhang, Alejandro F. Frangi
In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i. e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA).