Semi-supervised learning offers an appealing solution for remote sensing (RS) image segmentation to relieve the burden of labor-intensive pixel-level labeling. However, RS images pose unique challenges, including rich multi-scale features and high inter-class similarity. To address these problems, this paper proposes a novel semi-supervised Multi-Scale Uncertainty and Cross-Teacher-Student Attention (MUCA) model for RS image semantic segmentation tasks. Specifically, MUCA constrains the consistency among feature maps at different layers of the network by introducing a multi-scale uncertainty consistency regularization. It improves the multi-scale learning capability of semi-supervised algorithms on unlabeled data. Additionally, MUCA utilizes a Cross-Teacher-Student attention mechanism to guide the student network, guiding the student network to construct more discriminative feature representations through complementary features from the teacher network. This design effectively integrates weak and strong augmentations (WA and SA) to further boost segmentation performance. To verify the effectiveness of our model, we conduct extensive experiments on ISPRS-Potsdam and LoveDA datasets. The experimental results show the superiority of our method over state-of-the-art semi-supervised methods. Notably, our model excels in distinguishing highly similar objects, showcasing its potential for advancing semi-supervised RS image segmentation tasks.

Recent advancements in large language models (LLMs) have provided a new avenue for chatbot development, while most existing research has primarily centered on single-user chatbots that focus on deciding "What" to answer after user inputs. In this paper, we identified that multi-user chatbots have more complex 3W design dimensions -- "What" to say, "When" to respond, and "Who" to answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), which is an LLM-based framework for chatbots specifically designed for group discussions. MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and Utterance Strategies Arbitrator. These modules jointly determine suitable response contents, timings, and the appropriate recipients. To make the optimizing process for MUCA easier, we further propose an LLM-based Multi-User Simulator (MUS) that can mimic real user behavior. This enables faster simulation of a conversation between the chatbot and simulated users, making the early development of the chatbot framework much more efficient. MUCA demonstrates effectiveness, including appropriate chime-in timing, relevant content, and positive user engagement, in goal-oriented conversations with a small to medium number of participants, as evidenced by case studies and experimental results from user studies.