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Kaixin Xu

4 accepted papers

2026

Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss

AAAI 2026technical

The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, so

Cited by 0SourcePDFScholar
2025

Global Graph Propagation with Hierarchical Information Transfer for Incomplete Contrastive Multi-view Clustering

AAAI 2025technical

Incomplete multi-view clustering has become one of the important research problems due to the extensive missing multi-view data in the real world. Although the existing methods have made great progress, there are still some problems: 1) most methods cannot effectively mine the information hidden in…

Cited by 0SourcePDFScholar
2024

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

CVPR 2024poster

Multi-modality large language models (MLLMs) as represented by GPT-4V have introduced a paradigm shift for visual perception and understanding tasks that a variety of abilities can be achieved within one foundation model. While current MLLMs demonstrate primary low-level visual abilities from the id…

2023

Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks

ICCV 2023poster

In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural Networks (DNNs) that addresses the challenge of optimizing the output distortion minimization while adhering to a target pruning ratio constraint. Our approach takes into account the collective influence of all…

Cited by 29PDFcodeScholar