With the advent of parameter-efficient fine-tuning techniques for pre-trained vision-language models, interest in adapting them for various incremental learning scenarios has grown, i.e., sequential increments on task, class, and domain. However, no high-performance incremental learning framework has integrated these three incremental scenarios to achieve Unified Incremental Learning (Uni-IL) in complex settings. In this work, we propose an incremental learning framework called Mixture of Attribute-Guided Experts (MAGE) to alleviate the long-term forgetting in vision-language model incremental learning. Our approach involves acquiring image attribute knowledge via LLMs to form an attribute pool. We match the most relevant attributes as inputs to the Mixture of Experts (MoE) to fine-tune the pre-trained CLIP. Then the expert routers learn to select specific expert combinations based on the data and attribute features, alleviating catastrophic forgetting. The attribute pool incorporates both domain and class knowledge, enabling our approach to adapt to the three types of incremental learning scenarios and thus facilitating unified incremental learning. Through extensive experiments on our newly proposed benchmark and existing incremental learning scenarios, the results demonstrate that our proposed method not only performs well on the new Uni-IL tasks but also consistently outperforms previous state-of-the-art methods. Source code is available at https://github.com/ElectricField/Uni-IL.

As communication bandwidth and resources are limited in network-based control systems, in order to reduce superfluous waste, it is necessary to design an event-triggered communication mechanism. In this paper, the problem of event-triggered state estimation is studied for fusion of multiple sensors with correlated noise. The noise of different sensors are cross-correlated and coupled with the system noise of the previous step and the same time step. An optimal state estimation algorithm based on iterative estimation of white noise estimator is presented, which makes full use of the observation information effectively. A numerical example is used to illustrate the effectiveness of the presented algorithm.