Work

Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation

Year: 2025

Type: conference-paper

Abstract: Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani, Mohammadali Mohammadkhani, Bardia Mohammadi, Omid Ghahroodi, Mahdieh Soleymani Baghshah, Ehsaneddin Asgari. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

DOI: 10.18653/v1/2025.findings-acl.861

Authors: Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani, Mohammadali Mohammadkhani, Bardia Mohammadi, Omid Ghahroodi, Mahdieh Soleymani Baghshah, Ehsaneddin Asgari

Institutions: University of Tehran, Saarland University, Sharif University of Technology, Max Planck Institute for Software Systems, K. N. Toosi University of Technology

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: Deutscher Akademischer Austauschdienst, Bundesministerium für Bildung und Forschung

OpenAlex ID: W4412887933

FWCI: 32.82

Cites:

Cited by: 42

Related to: 10