arXiv AI July 14, 2026 By Haoran Ma, Yinfeng Yu, Liejun Wang

MRUF: Multi-granularity Routing with Uncertainty-Aware Fusion for Robust Multimodal Sentiment Analysis

arXiv:2607. 10599v1 Announce Type: new Abstract: Multimodal sentiment analysis relies on language, visual, and acoustic cues, but utterance-level modality quality may vary due to occlusion, background noise, motion blur, or imperfect transcripts, causing conventional fusion to over-trust unreliable modalities.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 5

Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations

arXiv:2608. 03611v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete.

By Chunlei Meng, Jacqueline J. Pang, Pengbin Feng, Zhenyu Yu, Chun Ouyang, Zhongxue Gan

nlp multimodal safety

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

1d ago

Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis

arXiv:2608. 16201v1 Announce Type: new Abstract: Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision.

By Shanshan Lin, Yuesheng Wu, Chao Chen, Yizhe Yang, Zhihao Chen, Zexian Yang, Xiangwen Liao

llms nlp multimodal benchmarks safety

arXiv Machine Learning

Jun 26

When Does Quality-Aware Multimodal Fusion Matter? A Leakage-Safe Diagnostic for Decision-Level Dependence

arXiv:2606. 26473v1 Announce Type: new Abstract: Many multimodal systems estimate the reliability of each modality and weight their contributions to the final prediction.

By Jaden Moon, Arvind Pillai, Andrew Campbell

nlp multimodal

arXiv AI

Aug 5

Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition

arXiv:2608. 03475v1 Announce Type: cross Abstract: Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant.

By Suraj Kumar, Mohnish Raj, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta

multimodal benchmarks

arXiv Machine Learning

Jun 3

Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals

arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.

By Jiyuan Liu, Liangwei Nathan Zheng, Wei Emma Zhang, Xinpei Wang, Weitong Chen

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Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations

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

Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis

When Does Quality-Aware Multimodal Fusion Matter? A Leakage-Safe Diagnostic for Decision-Level Dependence

Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition

Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals