most citedMultilinear Principal Component Analysis Network for Tensor Object Classification

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2014

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…

cs.CV20143 cited

Multilinear Principal Component Analysis Network for Tensor Object Classification

Rui Zeng, Jiasong Wu, Zhuhong Shao +2

The recently proposed principal component analysis network (PCANet) has been proved high performance for visual content classification. In this letter, we develop a tensorial exten…

cs.CV20142 cited

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…