65 citations · 90 across the 6 of their papers we have counts for
8 papers
LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning
Jiang Yuan, JI Ma, Bo Wang +2
Implicit degradation estimation-based blind super-resolution (IDE-BSR) hinges on extracting the implicit degradation representation (IDR) of the LR image and adapting it to LR imag…
How Far Are We from Generating Missing Modalities with Foundation Models?
Guanzhou Ke, Bo Wang, Guoqing Chao +2
Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutions for missing modality reconstruction…
Incomplete Multi-view Clustering via Diffusion Contrastive Generation
Yuanyang Zhang, Yijie Lin, Weiqing Yan +6
Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to ad…
Knowledge Bridger: Towards Training-free Missing Modality Completion
Guanzhou Ke, Shengfeng He, Xiao Li Wang +5
Previous successful approaches to missing modality completion rely on carefully designed fusion techniques and extensive pre-training on complete data, which can limit their genera…
Rethinking Multi-view Representation Learning via Distilled Disentangling
Guanzhou Ke, Bo Wang, Xiaoli Wang +1
Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth…
Disentangling Multi-view Representations Beyond Inductive Bias
Guanzhou Ke, Yang Yu, Guoqing Chao +3
Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representatio…
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
Guanzhou Ke, Guoqing Chao, Xiaoli Wang +3
The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facili…
MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion
Guanzhou Ke, Yongqi Zhu, Yang Yu
Multi-view representation learning is essential for many multi-view tasks, such as clustering and classification. However, there are two challenging problems plaguing the community…