arXiv AI

SHAP-Weighted Cross-Modal Expert Fusion for Emotion and Sentiment Recognition: Evidence and Limits

arXiv:2607. 08573v1 Announce Type: new Abstract: Multimodal emotion and sentiment recognition is commonly addressed by early fusion, which concatenates modalities before classification, or late fusion, which combines independently trained unimodal predictors.

Hugging Face Trending Papers

Jul 23

Quality-Aware Multimodal Fusion Reveals Implicit Identity in Valence-Arousal Features

Conventional face recognition relies on static appearance cues and degrades in unconstrained settings with expression variation, occlusion, and poor lighting. We hypothesize that audiovisual expression dynamics carry identity-discriminative information complementary to static appearance, and that extracting this signal requires multimodal representations robust to the variable input quality of in-the-wild video.

multimodal

arXiv Machine Learning

Jul 7

Beyond Modality Fusion: Deep Ensembles for Multimodal Classification

arXiv:2607. 05019v1 Announce Type: new Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks.

By Ilya Burenko, Dmitry Vetrov

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

multimodal benchmarks

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

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 Machine Learning

Jul 31

TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification

arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.

By Yu Chang, Anzhe Cheng, Chenwei Wu, Zhuoran Wang, Jiahao Chen, Tamoghna Chattopadhyay, Sophia I. Thomopoulos, Paul M. Thompson, Liyue Shen, Paul Bogdan

multimodal benchmarks

arXiv AI

Aug 11

Expert-Guided Multimodal Fusion for Unified Emotion and Sentiment Analysis

arXiv:2601. 07565v2 Announce Type: replace-cross Abstract: Multimodal emotion understanding requires the integration of heterogeneous data sources, including text, audio, and visual modalities, while simultaneously addressing discrete emotion recognition and continuous sentiment analysis.

By Jiaqi Qiao, Xinran Li, Yifan Lyu, Xiujuan Xu, Liu Yu

llms nlp 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 10

Ranking Image Fusion the Way Humans Do: A Learned Pairwise Preference Measure for Infrared-Visible Fusion Assessment

arXiv:2608. 01301v3 Announce Type: replace-cross Abstract: Infrared-visible image fusion (IVIF) has no ideal fused reference, so algorithms are ranked by scalar objective metrics that formalize proxies for information transfer, structure, or source similarity.

By Haoran Liu, Mingzhe Liu, Peng Li, Guibin Zan

benchmarks

arXiv AI

Jun 2

UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment

arXiv:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.

By Zheng Wang, Shuo Wang, Junhong Wang

llms fine-tuning efficiency multimodal benchmarks safety

arXiv Machine Learning

Jul 24

Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion

arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.

By Mohammad Raahemi, Ali Sekhavati, Alireza Maleki, Hamid Nasiri

multimodal benchmarks safety data-engineering

arXiv Machine Learning

Aug 5

DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing

arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.

By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen

rag multimodal benchmarks

Related stories