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.
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.
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...
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.
arXiv:2608. 09986v1 Announce Type: new Abstract: Most existing multimodal sentiment analysis approaches assume access to complete multimodal inputs.
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.
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.
arXiv:2608. 20019v1 Announce Type: new Abstract: Incomplete multimodal sentiment analysis has garnered significant attention in recent years.
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.
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.
arXiv:2608. 19971v1 Announce Type: new Abstract: Multimodal sentiment analysis aims to infer affective states by integrating language, visual, and acoustic cues.
The paper introduces Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework that extends Symmetric Nonnegative Matrix Tri-Factorization to supervised prediction across populations of multimodal brain graphs. SD3MF learns deep hierarchical factorizations for each modality and a shared latent representation, jointly optimizing graph reconstruction and prediction while enabling data-driven multimodal fusion. Experiments on multimodal connectome datasets demonstrate that SD3MF outperforms strong deep learning baselines such as CNNs and GNNs, providing biologically interpretable insights through community-level interaction matrices.
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...