arXiv Machine Learning

Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion

arXiv:2506. 22036v2 Announce Type: replace Abstract: With the increasing multimodal knowledge privatization requirements, multimodal knowledge graphs in different institutes are usually decentralized, lacking of effective collaboration system with both stronger reasoning ability and transmission safety guarantees.

arXiv AI

Jul 20

MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts for Multimodal Knowledge Graph Completion

arXiv:2607. 15592v1 Announce Type: new Abstract: Multimodal Knowledge Graph Completion (MKGC) requires inferring missing entities from structural, textual, and visual cues.

By Xu Hou, Meiyu Liang, Wei Huang, Yawen Li, Zhe Xue, Wu Liu, Guanhua Ye, Lei Shi, Kangkang Lu

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 21

RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion

arXiv:2604. 25693v2 Announce Type: replace Abstract: Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to conduct both retrieval over the full entity set and final link prediction.

By Guanglin Niu, Bo Li

llms rag diffusion efficiency multimodal benchmarks

arXiv Machine Learning

Jul 28

FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities

arXiv:2607. 23245v1 Announce Type: cross Abstract: Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective collaboration across clients.

By Haochen Liang, Jie Zhang, Hideya Ochiai

multimodal safety

arXiv Machine Learning

Aug 3

MMFGU: Multimodal Federated Graph Unlearning

arXiv:2607. 28708v1 Announce Type: new Abstract: Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data.

By Haodong Lu, Zekai Chen, Weiwei Ji, Shihao Li, Xunkai Li, Xun Wu, Yinlin Zhu, Rong-Hua Li

multimodal

arXiv Machine Learning

Jun 9

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

arXiv:2606. 09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images.

By Zekai Chen, Miao Zhang, Jiayang Xing, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang

llms multimodal benchmarks

arXiv AI

Aug 7

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.

By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang

llms rag multimodal

arXiv AI

Jul 21

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

arXiv:2507. 20804v3 Announce Type: replace Abstract: Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge.

By Xueyao Wan, Hang Yu

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jul 20

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.

By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

fine-tuning multimodal safety

arXiv Machine Learning

Aug 4

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.

By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan

fine-tuning multimodal benchmarks

arXiv AI

Aug 6

C$^2$MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning

arXiv:2608. 04013v1 Announce Type: cross Abstract: Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs.

By Yuntao Shou, Tao Meng, Wei Ai, Keqin Li

multimodal benchmarks

arXiv AI

Aug 12

MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis

arXiv:2608. 09986v1 Announce Type: new Abstract: Most existing multimodal sentiment analysis approaches assume access to complete multimodal inputs.

By Yuhua Wen, Yingying Zhou, Qifei Li, Yingming Gao, Zhengqi Wen, Jianhua Tao, Ya Li

nlp multimodal safety

arXiv Machine Learning

Jun 16

Unsupervised Learning for Missing Modalities in Multimodal Learning

arXiv:2606. 15743v1 Announce Type: new Abstract: This paper addresses the missing-modality challenge in multi-modal learning by introducing Unsupervised Learning for Missing Modalities in Multi-Modal Learning (UL4M4), a flexible framework that imputes missing feature embeddings in a task-independent manner before supervised prediction.

By Hassan Ismkhan, Hamid Bouchahcia

rag multimodal benchmarks

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