Artificial Intelligence for Multimedia Analysis, Generation, and Intelligent Services

Topic Information

Dear Colleagues,

We are pleased to invite you to submit your work to this Topic titled Artificial Intelligence for Multimedia Analysis, Generation, and Intelligent Services. Our discussion is centered around a key research question: how can modern artificial intelligence and multimodal foundation models support reliable, efficient, and smart analysis as well as delivery of multimedia information and services?

Nowadays, images, videos, audio, text and their multimodal combinations have become the main carrier for information communication. Additionally, large volumes of multimedia data keep being produced by social media platforms, streaming services, autonomous equipment, medical imaging systems and various IoT devices. This Topic spans the complete technical workflow: starting from multimedia representation and comprehension; moving on to content generation, retrieval, and recommendation; and finally reaching end‑user‑facing intelligent services. Recent progress in multimodal foundation models and generative AI has greatly pushed forward cross‑modal reasoning, content creation and large‑scale vision–language analysis. Even so, real‑world practical deployment still brings many unresolved hurdles, such as model efficiency, trustworthiness, privacy risks and generalization performance.

We welcome original research articles, review papers and perspective manuscripts covering relevant theories, technical approaches and real‑world applications. The scope is grouped into five thematic clusters as follows:

1. Multimedia representation and understanding: image, video, audio, and text analysis; self‑supervised learning; multimodal representation learning; and temporal and spatial modeling.
2. Multimodal and foundation models: vision–language/audio–language/video–language models; multimodal large language models; multimodal reasoning; model adaptation and efficient fine‑tuning; and multimodal agents.
3. Generation, retrieval, and recommendation: generative multimedia content synthesis and editing, cross‑modal retrieval, semantic search, and recommendation and personalization.
4. Applications and intelligent services: social‑media‑oriented services, AI‑empowered education, intelligent healthcare services, entertainment scenarios, smart‑city systems, and autonomous and IoT‑driven applications.
5. Trustworthy and deployable multimedia AI: model explainability; adversarial robustness; bias and fairness issues; privacy‑preserving multimedia learning (including federated and distributed learning); deepfake detection and provenance tracking for synthetic media; media watermarking and content authenticity verification; lightweight and efficient multimodal models; edge‑AI and on‑device multimedia intelligence; real‑time multimodal data processing; energy‑friendly AI deployment, multimodal datasets, benchmarks, and model evaluation protocols; and weakly‑supervised and self‑supervised learning, domain adaptation, and model generalization.

This Topic hopes to encourage cross‑disciplinary communication, promote practical methodological innovation, and address open challenges along the full multimedia intelligence pipeline. We invite contributions that report new algorithms and hands‑on deployment experience, as well as forward‑looking viewpoint studies.

Prof. Dr. Teng Li
Prof. Dr. Fei Hao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Analytics analytics	-	4.3	2022	24.2 Days	CHF 1200	Submit
Big Data and Cognitive Computing BDCC	5.3	11.4	2017	23.3 Days	CHF 1800	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Multimedia multimedia	-	-	2025	15.0 days *	CHF 1000	Submit
Multimodal Technologies and Interaction mti	3.3	6.6	2017	22.8 Days	CHF 1800	Submit

* Median value for all MDPI journals in the first half of 2026.

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