arXiv Computation and Language

Divide and Conquer: Mixture-of-Bottleneck Experts in Informative Ordinal Space for Video-based Multimodal Sentiment Analysis

The paper proposes a Mixture-of-Bottleneck (MoB) framework for video-based multimodal sentiment analysis that treats sentiment as an ordinal regression problem, splitting it into polarity recognition and intensity prediction. MoB assigns modality‑specific latent experts to each sub‑task, learns compact, task‑relevant representations via an information bottleneck, and fuses these experts with a multimodal bottleneck routing module and hard mining strategy. Experiments on four datasets and language models demonstrate that MoB captures fine‑grained intra‑ and inter‑modal dynamics, improving performance and enabling more trustworthy localization of nuanced sentiment signals.

arXiv Computer Vision

Oct 21

Text-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis

The paper introduces TAME, a Text‑Anchored Modality Experts framework that treats language as a fixed, always‑active expert and routes audio‑textual and video‑textual cross‑modal branches as dynamic auxiliary experts. It incorporates lightweight Prophet Heads to estimate sample‑level uncertainty for each modality, guiding a reliability‑aware routing mechanism for expert aggregation. Experiments on CMU‑MOSI, CMU‑MOSEI, and CH‑SIMS show that TAME achieves state‑of‑the‑art performance while reducing parameters by up to 78.7% and FLOPs by up to 41.2% compared to a mixture‑of‑experts baseline.

By Xiaohua Huang, Bingrui Lei, Xiuyan hao, Ling Zhou, Yuan Zong, Wenming Zheng, Guoying Zhao

nlp multimodal benchmarks

arXiv AI

Sep 1

Multimodal Adaptive Expert Selection with Text Routing and Ordinal Prototype Optimization for Sentiment Analysis

arXiv:2608.30726v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) is a fundamental component of affective computing that aims to decipher complex emotional states by integrating ver...

By Xiaode Chen, Jiakang Yu, Hongtao Deng, Huina Qu, Xun Zhu, Yinxia Lou

diffusion nlp multimodal benchmarks safety

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 Machine Learning

Sep 29

Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis

Reliability-aware Cross-sample Enhancement (RCE) is a framework for multimodal sentiment analysis that tackles noise and missing modalities by first applying an adaptive variational information bottleneck to compress unreliable modality information. It then retrieves high‑confidence, semantically consistent neighbors from a large candidate pool to enrich current representations, and finally fuses cross‑modal interactions through a multilevel reliability‑aware mechanism. Experiments show RCE consistently outperforms state‑of‑the‑art methods in full, noisy, and missing‑modality scenarios.

By Menghua Jiang, Haokai Gao, Xiangui Kang, Haifeng Hu, Sijie Mai

nlp multimodal benchmarks

arXiv Computer Vision

Oct 23

I$^2$-KAN: Interpretable Intra- and Inter-Modal Multi-Pathway Representation Learning for Multimodal Sentiment Analysis

I$^2$-KAN is a new multimodal sentiment analysis framework that separates intra‑modal and pairwise inter‑modal information into distinct pathways. Each pathway is refined by a Task‑Decoupled Linear‑Gated Denoising Bottleneck and fused using a structured Kolmogorov‑Arnold Network, enabling explicit pathway associations and multi‑level interpretation. Experiments on CMU‑MOSI, CMU‑MOSEI, and CH‑SIMS v2 show strong performance and reveal that a modality’s standalone predictive power does not always reflect its interaction‑conditioned utility.

By Xiaoting Xie, Feng Qiao, Xianwei Zhang, Hailong Chen, Kaiwen Zheng, Joemon M. Jose, Xuri Ge, Yujun Li

nlp multimodal benchmarks safety

arXiv Machine Learning

Aug 18

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 AI

Jul 9

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.

By Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu

llms nlp efficiency multimodal benchmarks

arXiv Computation and Language

Sep 25

SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data

SemMSA introduces a latent semantic‑aided framework for multimodal sentiment analysis that leverages large language models to generate rich sentiment‑relevant semantics. The method employs Cross‑modal Semantic Refinement (CSR) to fuse visual, acoustic, and language features in a frozen LLM embedding space, and Cross‑modal Spectral Alignment (CSA) to align these refined semantics with all modalities via spectral enhancement of kernel Gram matrices. Experiments on SIMS, MOSI, and MOSEI benchmarks show that SemMSA achieves state‑of‑the‑art performance.

By Wenhao Li, Zhibin Wu, Chong Xiao, Qiangchang Wang

llms rag nlp multimodal benchmarks safety

arXiv Machine Learning

Oct 27

ConsMSA: Semantic Distribution Consistency Learning for Multimodal Sentiment Analysis

arXiv:2610.31731v1 Announce Type: new Abstract: Multimodal sentiment analysis (MSA) aims to predict human sentiments by integrating signals from different modalities such as text, video, and audio. H...

By Pan Wang, Lipeng Ke, Huajun Ying, Pritish Mohapatra, Rohan Sarkar, Suresh Lakhani, Sankar Venkataraman, Jingtong Hu

nlp 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 AI

Aug 21

Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination

arXiv:2608. 20019v1 Announce Type: new Abstract: Incomplete multimodal sentiment analysis has garnered significant attention in recent years.

By Kaixin Xu, NaiJin Liu, Yulin Kang, Tangyue Jin, Zixuan Yu, Wenxi Zhao, Yibei Liu, Qianle Zhang, Yangyang Wu, Mengying Zhu, Meng Xi

nlp 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

Related stories