5 citations · 12 across the 17 of their papers we have counts for
17 papers
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…
Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration
Minheng Chen, Zhirun Zhang, Shuheng Gu +1
We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which…
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
Jiacheng Shi, Yuting He, Youyong Kong +4
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to d…
CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images
Song Wang, Yuting He, Youyong Kong +7
Renal compartment segmentation on CT images targets on extracting the 3D structure of renal compartments from abdominal CTA images and is of great significance to the diagnosis and…
EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation
Yuting He, Rongjun Ge, Xiaoming Qi +6
3D complete renal structures(CRS) segmentation targets on segmenting the kidneys, tumors, renal arteries and veins in one inference. Once successful, it will provide preoperative p…
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images
Yuting He, Tiantian Li, Guanyu Yang +6
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region cons…
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…
CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language
Jiasong Wu, Xuan Li, Taotao Li +5
Previous audio-visual speech separation methods use the synchronization of the speaker's facial movement and speech in the video to supervise the speech separation in a self-superv…
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…
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…
Phase-only signal reconstruction by MagnitudeCut
Jiasong Wu, Jieyuan Liu, Youyong Kong +3
In this paper, we present a new algorithm, called MagnitudeCut, for recovering a signal from the phase of its Fourier transform. We casted our recovering problem into a new convex…