no code implementations • 15 Mar 2024 • Xiangtian Xue, Jiasong Wu, Youyong Kong, Lotfi Senhadji, Huazhong Shu
We transcend the limitation of traditional attention mechanisms that only focus on existing visual features by introducing deformable feature alignment to hierarchically refine spatial positioning fused with multi-scale visual and linguistic information.
no code implementations • 14 Mar 2024 • Xiangtian Xue, Jiasong Wu, Youyong Kong, Lotfi Senhadji, Huazhong Shu
Referring object removal refers to removing the specific object in an image referred by natural language expressions and filling the missing region with reasonable semantics.
1 code implementation • 13 Mar 2024 • Fuzhi Wu, Jiasong Wu, Youyong Kong, Chunfeng Yang, Guanyu Yang, Huazhong Shu, Guy Carrault, Lotfi Senhadji
Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged.
1 code implementation • 30 Oct 2021 • Jiasong Wu, Qingchun Li, Guanyu Yang, Lei LI, Lotfi Senhadji, Huazhong Shu
The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs.
no code implementations • 28 Jul 2020 • Jiasong Wu, Jing Zhang, Fuzhi Wu, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu
In order to solve or alleviate the synchronous training difficult problems of GANs and VAEs, recently, researchers propose Generative Scattering Networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain the features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate the image.
1 code implementation • 21 Jul 2020 • Jiasong Wu, Xuan Li, Taotao Li, Fanman Meng, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu
We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem.
no code implementations • 20 Mar 2019 • Jiasong Wu, Ling Xu, Youyong Kong, Lotfi Senhadji, Huazhong Shu
In recent years, the deep complex networks (DCNs) and the deep quaternion networks (DQNs) have attracted more and more attentions.
no code implementations • 6 Mar 2019 • Jiasong Wu, Hongshan Ren, Youyong Kong, Chunfeng Yang, Lotfi Senhadji, Huazhong Shu
Although convolutional neural network (CNN) has made great progress, large redundant parameters restrict its deployment on embedded devices, especially mobile devices.
no code implementations • 27 Feb 2019 • Jiasong Wu, Fuzhi Wu, Qihan Yang, Youyong Kong, Xilin Liu, Yan Zhang, Lotfi Senhadji, Huazhong Shu
One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs.
no code implementations • 27 Feb 2019 • Jinpeng Xia, Jiasong Wu, Youyong Kong, Pinzheng Zhang, Lotfi Senhadji, Huazhong Shu
Although Convolutional Neural Networks (CNNs) achieve effectiveness in various computer vision tasks, the significant requirement of storage of such networks hinders the deployment on computationally limited devices.
no code implementations • 30 Jun 2018 • Li Liu, Jiasong Wu, Dengwang Li, Lotfi Senhadji, Huazhong Shu
Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is significantly improved in fractional scattering domain.
no code implementations • 24 Nov 2017 • Xiao Dong, Jiasong Wu, Ling Zhou
The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear.
no code implementations • 30 Oct 2017 • Xiao Dong, Jiasong Wu, Ling Zhou
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective.
no code implementations • 22 Feb 2017 • Jiasong Wu, Shijie Qiu, Youyong Kong, Yang Chen, Lotfi Senhadji, Huazhong Shu
In this paper, we propose a new simple and learning-free deep learning network named MomentsNet, whose convolution layer, nonlinear processing layer and pooling layer are constructed by Moments kernels, binary hashing and block-wise histogram, respectively.
no code implementations • 3 Mar 2016 • Jiasong Wu, Shijie Qiu, Youyong Kong, Longyu Jiang, Lotfi Senhadji, Huazhong Shu
The principal component analysis network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases.
no code implementations • 20 Dec 2015 • Dan Wu, Jiasong Wu, Rui Zeng, Longyu Jiang, Lotfi Senhadji, Huazhong Shu
In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed.
no code implementations • 5 Mar 2015 • Rui Zeng, Jiasong Wu, Zhuhong Shao, Yang Chen, Lotfi Senhadji, Huazhong Shu
The Principal Component Analysis Network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases.
no code implementations • 5 Nov 2014 • Rui Zeng, Jiasong Wu, Lotfi Senhadji, Huazhong Shu
The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms.
no code implementations • 5 Nov 2014 • Rui Zeng, Jiasong Wu, Zhuhong Shao, Lotfi Senhadji, Huazhong Shu
The recently proposed principal component analysis network (PCANet) has been proved high performance for visual content classification.
no code implementations • 24 Jul 2014 • Jiasong Wu, Longyu Jiang, Xu Han, Lotfi Senhadji, Huazhong Shu
Texture plays an important role in many image analysis applications.