Human-AI cooperative classification (HAI-CC) aims to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by leveraging both human expertise and AI capabilities. Current HAI-CC methods primarily focus on learning-to-defer (L2D), where decisions are deferred to human experts when AI is not confident, and learning-to-complement (L2C), where AI and human experts make predictions cooperatively. However, existing research in both L2D and L2C has not effectively been explored under diverse expert knowledge to improve decision-making, particularly when constrained by the operation cost of human involvement. In this paper, we address this research gap by proposing the Coverage-constrained Learning to Defer and Complement with Specific Experts (CL2DC) method. In particular, CL2DC assesses input data before making final decisions through either AI prediction alone or by deferring to or complementing a specific human expert. Furthermore, we propose a coverage-constrained optimisation to control the cooperation cost, ensuring it approximates a target probability for AI-only selection. This approach enables an effective assessment of system performance within a specified budget. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that CL2DC achieves superior performance compared to state-of-the-art HAI-CC methods.

Precipitation nowcasting plays an important role in our life. Many deep learning-based methods are proposed for precipitation nowcasting by predicting radar echo sequence over the past years, and achieving better performance than traditional approaches. However, all of them are based on a static model, which is trained in offline learning and does not adapt to real-time changing precipitation data. Recently, online incremental learning (OIL) has been proposed to dynamically update the model by continually learning new data and preventing the forgetting of historical knowledge in an online fashion. While effective, existing OIL approaches that focus on a classification task are not suitable for the regression task of precipitation nowcasting. To fill this gap, we try to propose a novel OIL framework for precipitation nowcasting. By analyzing its characteristics, we find three challenges: 1) the distributions of radar echo maps in different rainfall events are different; 2) in each rainfall event, there is always an inevitable delay between the timestamps of the training and testing samples; and 3) the real-time requirement for model prediction is very high, which has strict limitations on the training speed of the model. Based on these observations, we propose a practical OIL framework based on gradient activation mapping (GAM). It can be mainly divided into three components: 1) recall training strategy (RTS) is used to eliminate the interference caused by distributions of different events; 2) iterative approximation training (IAT) is designed to align the timestamps of the training and testing; and 3) moreover, we propose gradient activation mapping weight (GAMW) to improve the training effectiveness. Extensive experiments show that the proposed framework can improve the performance of the model stably and effectively. Especially in those heavy rainfall regions where it usually causes more threat to human activity, the improvement is significant.