会议信息

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

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MMAL
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截稿日期:
2026-10-15 还有 34 天 Extended
通知日期:
2026-11-15
会议日期:
2027-01-15
会议地点:
Singapore
届数:
1
主办方:
浏览: 625   关注: 1   参加: 0

会伴指数 (CP-I)

39.0 / 100
全站第 5,324 名 / 共 5,680 个会议 · 前 94%

人工智能与机器学习 第 685 / 739

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
19
社区关注 (10%)
25
资料公开度 (15%)
35

用到的输入: 有据可查的届次：1 · 在会伴关注它的研究者：1 人 · 过去 24 个月打开过本页的研究者：10 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-11

征稿

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
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