Progress, Challenges, and Future Trends in Computer Vision and Pattern Recognition

Dear Colleagues,

Computer vision and pattern recognition have become cornerstone technologies in the modern era of artificial intelligence, driving innovation across diverse domains such as autonomous systems, medical imaging, robotics, smart manufacturing, and environmental monitoring. With the rapid progress of deep learning and multimodal perception, we are witnessing a paradigm shift from task-specific algorithms toward generalizable, interpretable, and trustworthy visual understanding systems.

Despite these remarkable advances, the field continues to face critical challenges, including ensuring robustness under complex environments, achieving efficient learning with limited or imbalanced data, addressing ethical and privacy concerns, and bridging the gap between low-level perception and high-level reasoning. In addition, the integration of vision with other modalities such as language, depth, and tactile information opens new opportunities for embodied intelligence and human–AI collaboration.

This Special Issue aims to bring together recent breakthroughs, emerging methodologies, and visionary perspectives in computer vision and pattern recognition. In this Special Issue, original research articles and reviews are welcome. Research areas may include but are not limited to the following:

• Image and video analysis, object detection, segmentation, and recognition;
• 3D vision, scene reconstruction, and embodied perception;
• Multimodal and cross-domain representation learning;
• Motion analysis, tracking, and behavior understanding;
• Generative models and foundation models for vision tasks;
• Self-supervised, semi-supervised, and few-shot learning;
• Adversarial robustness, interpretability, and trustworthy AI;
• Vision–language models and visual reasoning;
• Medical and industrial applications of computer vision;
• Benchmark datasets, large-scale evaluation, and open-source frameworks.

Through this collection, we hope to inspire a deeper understanding of the progress made, highlight the ongoing challenges, and outline future trends shaping the next generation of intelligent visual systems.

Dr. Qing Cai
Dr. Jinxing Li
Guest Editors

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