5 citations · 8 across the 10 of their papers we have counts for
12 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…