AI 大模型

多模态大模型

跨文本、图像、视频、音频等模态的大模型与学习方法。

2025-09-05 至 2025-09-05 共收录 5 篇信号源：cs.CV, cs.CL, cs.AI, cs.MM, eess.AS

1. 视频多模态 5 篇

2509.04254 2025-09-05 cs.HC 82%

MuMTAffect: A Multimodal Multitask Affective Framework for Personality and Emotion Recognition from Physiological Signals

Meisam Jamshidi Seikavandi, Fabricio Batista Narcizo, Ted Vucurevich, Andrew Burke Dittberner, Paolo Burelli

专题命中视频多模态：multimodal(title,abstract)；cross-modal(abstract)

2509.04330 2025-09-05 cs.IR 78%

Temporal Interest-Driven Multimodal Personalized Content Generation

Tian Miao

专题命中视频多模态：multimodal(title,abstract)

2509.04210 2025-09-05 cs.CE cs.LG 78%

COBRA: Multimodal Sensing Deep Learning Framework for Remote Chronic Obesity Management via Wrist-Worn Activity Monitoring

Zhengyang Shen, Bo Gao, Mayue Shi

机构 * Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK（帝国理工学院电子与电气工程系）； Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK（牛津大学生物医学工程研究所）

专题命中视频多模态：multimodal(title,abstract)

Comments 19 pages, 4 figures. *Correspondence: m.shi16@imperial.ac.uk. Accepted by the IUPESM World Congress on Medical Physics and Biomedical Engineering 2025

2507.19346 2025-09-05 cs.LG 78%

Short-Form Video Recommendations with Multimodal Embeddings: Addressing Cold-Start and Bias Challenges

Andrii Dzhoha, Katya Mirylenka, Egor Malykh, Marco-Andrea Buchmann, Francesca Catino

机构 * Zalando SE Berlin Germany（泽尔安多德国分公司）； Zalando Switzerland AG Zürich Switzerland（泽尔安多瑞士分公司）

专题命中视频多模态：multimodal(title,abstract)

2509.04117 2025-09-05 cs.CV 57%

DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset

Mustafa Sakhai, Kaung Sithu, Min Khant Soe Oke, Maciej Wielgosz

机构 * Faculty of Computer Science, Electronics and Telecommunications（计算机科学与电子技术学院）； AGH University of Science and Technology（AGH科技大学）

专题命中视频多模态：multimodal(abstract)；分类 cs.CV

Comments 12 pages, 8 figures, 3 tables; dataset descriptor paper introducing DVS-PedX (synthetic-and-real event-based pedestrian dataset with baselines) External URL: https://doi.org/10.5281/zenodo.17030898