46 citations · 76 across the 24 of their papers we have counts for
14 papers · 1 filter
ST-LDM: A Universal Framework for Text-Grounded Object Generation in Real Images
Xiangtian Xue, Jiasong Wu, Youyong Kong +2
We present a novel image editing scenario termed Text-grounded Object Generation (TOG), defined as generating a new object in the real image spatially conditioned by textual descri…
Rethinking Referring Object Removal
Xiangtian Xue, Jiasong Wu, Youyong Kong +2
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. To ad…
Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks
Fuzhi Wu, Jiasong Wu, Youyong Kong +5
Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information…
Generative networks as inverse problems with fractional wavelet scattering networks
Jiasong Wu, Jing Zhang, Fuzhi Wu +4
Deep learning is a hot research topic in the field of machine learning methods and applications. Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) provide…
Deep Octonion Networks
Jiasong Wu, Ling Xu, Youyong Kong +2
Deep learning is a research hot topic in the field of machine learning. Real-value neural networks (Real NNs), especially deep real networks (DRNs), have been widely used in many r…
Compressing complex convolutional neural network based on an improved deep compression algorithm
Jiasong Wu, Hongshan Ren, Youyong Kong +3
Although convolutional neural network (CNN) has made great progress, large redundant parameters restrict its deployment on embedded devices, especially mobile devices. The recent c…
Fractional spectral graph wavelets and their applications
Jiasong Wu, Fuzhi Wu, Qihan Yang +5
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 graph…
Modulated binary cliquenet
Jinpeng Xia, Jiasong Wu, Youyong Kong +3
Although Convolutional Neural Networks (CNNs) achieve effectiveness in various computer vision tasks, the significant requirement of storage of such networks hinders the deployment…
Fractional Wavelet Scattering Network and Applications
Li Liu, Jiasong Wu, Dengwang Li +2
Objective: The present study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scatterin…
MomentsNet: a simple learning-free method for binary image recognition
Jiasong Wu, Shijie Qiu, Youyong Kong +3
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 construct…
PCANet: An energy perspective
Jiasong Wu, Shijie Qiu, Youyong Kong +3
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 vario…
Color Image Classification via Quaternion Principal Component Analysis Network
Rui Zeng, Jiasong Wu, Zhuhong Shao +3
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 vario…
Tensor object classification via multilinear discriminant analysis network
Rui Zeng, Jiasong Wu, Lotfi Senhadji +1
This paper proposes a multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, known as tensor objects. The MLDANet is a variation of li…
Performance evaluation of wavelet scattering network in image texture classification in various color spaces
Jiasong Wu, Longyu Jiang, Xu Han +2
Texture plays an important role in many image analysis applications. In this paper, we give a performance evaluation of color texture classification by performing wavelet scatterin…