Conference Information

MMAL 2027: International Conference on Multimodal Artificial Intelligence and Machine Learning

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Submission Date:
2026-10-15 Due in 34 days Extended
Notification Date:
2026-11-15
Conference Date:
2027-01-15
Location:
Singapore
Years:
1
Organizer:
Viewed: 621   Tracked: 1   Attend: 0

Conference Partner Index (CP-I)

39.0 / 100
Ranked #5,324 of 5,680 conferences · Top 94%

#685 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
19
Community attention (10%)
25
Public record completeness (15%)
35

Inputs used: Editions on record: 1 · Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 10

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-11

Call For Papers

MMAL 2027 (International Conference on Multimodal Artificial Intelligence and Machine Learning) is an academic conference held in Singapore on 2027-01-15. The paper submission deadline is 2026-10-15 (extended). Acceptance notifications are sent on 2026-11-15.

The MMAL 2027 will bring together leading academic scientists, researchers and scholars in the fields of multimodal artificial intelligence and machine learning of interest from around the world. Prospective authors are invited to contribute high-quality original research papers to MMAL 2027. All the accepted papers will be included in the conference proceedings, and will be submitted to EI Compendex, Scopus for indexing. Potential topics include, but are not limited to: 1.Foundations and Theories of Multimodal AI Multimodal representation learning and cross-modal alignment Unified multimodal modeling theories and architectures Pretraining and post-training methods for multimodal large models World models and physical law learning Self-supervised, semi-supervised, and few-shot multimodal learning Explainable and trustworthy multimodal learning Robust learning and uncertainty modeling Cross-modal generation and reasoning 2.Autonomous Agents, Multi-Agent Collaboration, and Human-AI Hybrid Decision-Making Autonomous agent theory and self-evolution mechanisms Multi-agent collaboration and swarm intelligence Autonomous planning and scheduling in complex dynamic environments Reinforcement learning and autonomous decision-making Multimodal intention understanding and human-robot interaction Human-AI hybrid augmented intelligence and human-in-the-loop mechanisms Embodied AI and physical interaction Safety, ethics, and alignment in human-AI collaboration 3.Multimodal AI System and Implementation Technologies Architectural design and optimization of multimodal intelligent systems Edge-cloud collaboration and engineering deployment Model lightweighting, compression, and embedded optimization Multimodal data governance, evaluation, and synthetic data Digital twin and simulation verification technologies Large-scale data processing and distributed learning System reliability, robustness, and fault diagnosis Privacy preservation and security protection technologies 4.Cutting-Edge Applications and Interdisciplinary Intersections of Multimodal AI Multimodal AI for scientific discovery (AI4Science) Multimodal learning in healthcare and bioinformatics Cross-disciplinary multimodal data analysis Industrial manufacturing and digital twin applications Multimodal AI in low-resource and real-world scenarios Cross-scenario transfer learning and domain adaptation Societal impacts and responsible AI in multimodal systems Intelligent decision support and industry applications
Last updated by WW Xu on

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