Multimodal Learning and Computer Vision for Intelligent Systems

Dear Colleagues,

As artificial intelligence technology enters a new stage of development enabled by large models, computer vision, as the core supporting technology for machine perception and intelligent cognition, has gradually moved from the single-image and video analysis paradigm to a new stage of multi-modal collaborative integration, cross-domain intelligent understanding, and scenario-based application. Traditional single-modal visual technology is limited by issues such as a single information dimension, weak anti-interference ability, and insufficient scene adaptability, making it difficult to meet the core requirements of high-precision perception, deep understanding, and autonomous decision-making of intelligent systems in complex real-world scenarios. However, multi-modal learning, by integrating visual, text, audio, point cloud, and sensor data from multiple heterogeneous sources, has broken through the technical bottleneck of single visual perception and has become a key core technology for connecting the entire chain of machine perception, cognition, decision-making, and execution, and widely empowering the iterative upgrade and application of various intelligent systems.

Currently, the theoretical innovation in the intersection of multi-modal learning and computer vision is continuously breaking through, and the technological iteration speed is significantly accelerating. Frontier technologies such as visual large models, cross-modal alignment, multi-source information fusion, self-supervised/weakly supervised learning, and embodied intelligent perception have completely revolutionized the traditional visual processing framework and effectively solved technical problems such as complex scene target detection, semantic segmentation, scene reconstruction, cross-modal retrieval, intelligent content generation, etc. At the same time, this technology system deeply empowers key areas such as autonomous driving, intelligent manufacturing, smart healthcare, security and emergency response, aerospace remote sensing, and smart home, becoming the core driving force for the realization of anthropomorphic perception, adaptive learning, and autonomous decision-making of the new generation of intelligent systems, and is an important breakthrough point for the transition from artificial intelligence research and development to industrial implementation. However, at the same time, the development of this field still faces many technical pain points and challenges such as heterogeneous and redundant multi-modal data, difficulties in cross-domain semantic alignment, insufficient small-sample generalization ability, difficulty in lightweight deployment with high real-time requirements, and insufficient scene robustness and security. These issues urgently require systematic and innovative research and exploration by the academic community and the industry.

To showcase the latest theoretical achievements, frontier technological breakthroughs, and typical application practices in the field of multi-modal learning and computer vision, focusing on the pain points of intelligent system application scenarios, and building a high-level academic exchange and technology transformation platform, this journal specially launches the "Application of Multi-Modal Learning and Computer Vision in Intelligent Systems" special column. This column aims to gather high-quality research results from experts and scholars, research teams, and industrial technicians in related fields, systematically review the development trajectory of the field, deeply analyze the core technical bottlenecks and future development trends, promote the improvement of the multi-modal visual intelligent theory system, technological innovation iteration, and integration of industrial implementation, and help the high-quality development of artificial intelligence perception technology and intelligent system applications.

This column focuses on soliciting original research papers, review papers, and technical application achievements in the following directions:

1. Multi-modal learning basic theory and core algorithms: cross-modal feature alignment, multi-source information fusion, self-supervised/weakly supervised/low-sample multi-modal learning, multi-modal large model fine-tuning and adaptation, multi-modal temporal learning and prediction, etc.;
2. High-performance computer vision key technologies: complex scene visual perception, high-precision target detection and segmentation, 3D scene reconstruction, video understanding and behavior analysis, image and video intelligent generation, visual defect detection, etc.;
3. Multi-modal visual intelligent system architecture and lightweight technology: multi-modal perception system design for edge, edge-cloud collaboration, large model lightweight and quantization deployment, real-time visual intelligent inference, embedded intelligent visual system development, etc.;
4. Multi-modal visual technology industry applications: autonomous driving environment perception, intelligent manufacturing visual quality inspection, smart healthcare image analysis, aerospace multi-modal interpretation, intelligent security and emergency judgment, smart home human–machine interaction, embodied intelligent visual perception, etc.
5. Frontier Areas and Security Research: Explainability and robustness optimization of multimodal visual models, adversarial defense, data security and privacy protection, construction of industry-specific multimodal datasets and evaluation systems, etc.

This column warmly welcomes researchers, university teachers and industry technical experts engaged in the fields of artificial intelligence, computer vision, multimodal learning, intelligent system engineering and related interdisciplinary areas to actively submit their latest research results, innovative technical solutions and industry application experiences. The column will select high-quality, innovative and practical academic achievements through strict and standardized peer review, creating a leading-edge academic platform in the field of multimodal visual intelligence technology, and providing strong support for the innovative development, academic exchanges and industrial transformation of multimodal visual intelligent technology.

Dr. Peishun Liu
Prof. Dr. Ruichun Tang
Guest Editors

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