This event is co-located with The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026
🗓️ Wed June 3 - Sun June 7, 2026Â
đź“ŤDenver Colorado, United StatesÂ
 Overview
Artificial intelligence is advancing rapidly and drawing wide attention. However, bringing visual intelligence into the real world remains a major challenge. Real-world data (RWD) typically exhibits multi-source heterogeneity, scarcity, and imbalance, often with missing values, noise, biases, limited labels, and constraints from complex environments. The AI4RWC workshop takes real-world visual data as its starting point and focuses on building, deploying, monitoring, and continuously improving vision systems in real-world scenarios. We welcome diverse approaches to these challenges, including few-/zero-shot learning, data-centric AI, continual/open-world learning, etc. We also invite knowledge-driven methods, as well as retrieval-augmented generation and large language models (LLMs/VLMs), and agent theory and applications that operate under real-world constraints. The workshop provides an interdisciplinary forum for researchers and practitioners worldwide to share cutting-edge research, innovative methods, and practical experience. Through keynotes, talks, and panels, it connects vision researchers and domain experts to foster collaboration toward trustworthy, effective, and impactful real-world computer vision. Through keynotes, technical presentations, and panel discussions, it will foster interaction between AI researchers and domain experts, cultivate long-term collaborations, and foster collaboration toward trustworthy, effective, and impactful AI in real environments.
Scope
The workshop welcomes contributions that address interdisciplinary and real-world challenges using computer vision and Artificial Intelligence technologies. We encourage research that bridges multiple domains, integrates diverse data sources, and operates effectively under real-world scenarios. Topics of interest include, but are not limited to:
Few-shot and zero-shot visual learning for real-world imageryÂ
Transfer learning and domain adaptation under real-world visual driftÂ
Data-centric computer vision for noisy, imbalanced, and heterogeneous datasetsÂ
Bias detection, concept drift, and robustness evaluation in deployed vision systemsÂ
Knowledge-enhanced and multimodal vision-language models (VLMs, VLLMs)Â
Retrieval-augmented perception and grounding for vision tasksÂ
Vision agents and autonomous visual reasoning in the wildÂ
Continual, open-world, and self-supervised visual learningÂ
Case studies on deploying vision systems (e.g., healthcare, remote sensing, cultural heritage)Â
Embodied Vision and Perception-Action Coupling: visual navigation, scene understanding, and sensorimotor groundingÂ
Simulation-to-Real Transfer: bridging synthetic and real visual domains for embodied intelligence
Supporting Institutions
