arXiv AI

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

arXiv:2606. 06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks.

arXiv Machine Learning

Jun 5

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.

By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang

rag diffusion multimodal benchmarks

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

22h ago

TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts

arXiv:2602. 21693v2 Announce Type: replace Abstract: Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by leveraging rich information inherent in other modalities.

By Jiafeng Lin, Yuxuan Wang, Huakun Luo, Jianmin Wang, Zhongyi Pei

llms multimodal benchmarks 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 AI

Jun 17

Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion

arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn

multimodal

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

Jun 10

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

By Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass

nlp multimodal

arXiv AI

Jun 11

Latent World Recovery for Multimodal Learning with Missing Modalities

arXiv:2606. 12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made.

By Hui Wang, Tianyu Ren, Joseph Butler, Christopher Baker, Karen Rafferty, Simon McDade

rag diffusion multimodal benchmarks 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

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

arXiv Machine Learning

Jul 14

Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction

arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.

By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta

multimodal safety

arXiv Machine Learning

Jul 9

Rethinking Multimodal Time-Series Forecasting Evaluation

arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.

By Haoxin Liu, Yichen Zhou, Rajat Sen, B. Aditya Prakash, Abhimanyu Das

multimodal benchmarks

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