Luobin Cui

Chengzhang Zhu, Luobin Cui, Ying Tang et al.

Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.

Luobin Cui, Yanlai Wu, Tang Ying et al.

Fatigue detection for human operators plays a key role in safety critical applications such as aviation, mining, and long haul transport. While numerous studies have demonstrated the effectiveness of high fidelity sensors in controlled laboratory environments, their performance often degrades when ported to real world settings due to noise, lighting conditions, and field of view constraints, thereby limiting their practicality. This paper formalizes a deployment oriented setting for real world fatigue detection, where high quality sensors are often unavailable in practical applications. To address this challenge, we propose leveraging knowledge from heterogeneous source domains, including high fidelity sensors that are difficult to deploy in the field but commonly used in controlled environments, to assist fatigue detection in the real world target domain. Building on this idea, we design a heterogeneous and multiple source fatigue detection framework that adaptively utilizes the available modalities in the target domain while exploiting diverse configurations in the source domains through alignment across domains and modality imputation. Our experiments, conducted using a field deployed sensor setup and two publicly available human fatigue datasets, demonstrate the practicality, robustness, and improved generalization of our approach across subjects and domains. The proposed method achieves consistent gains over strong baselines in sensor constrained scenarios.