Traditional machine societies rely on data-driven learning, overlooking interactions and limiting knowledge acquisition from model interplay. To address these issues, we revisit the development of machine societies by drawing inspiration from the evolutionary processes of human societies. Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Coevolution (SC). Compared to most existing methods focused on knowledge distillation and multi-task learning, our work addresses a more challenging problem: not only enhancing the capacity to solve new downstream tasks but also improving the performance of existing tasks through inter-model interactions. Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks. Specifically, we introduce the dynamic hierarchical collaboration and dynamic selective collaboration modules to enable dynamic and effective interactions among models, allowing them to acquire knowledge from these interactions. Finally, we explore potential future applications of combining SL and SC, discuss open questions, and propose directions for future research, aiming to spark interest in this emerging and exciting interdisciplinary field. Our code will be publicly available at https://github.com/yxjdarren/SC.

The detection and statistics of damaged buildings are crucial for rescue work, especially when detecting object-level and small buildings. Existing detection technologies mainly focus on pixel-level analysis, often ignoring the significance of object-level analysis and the identification of small buildings. To address these challenges, a Cross-Temporal Progressive Enhancement Model (CTPEM) is proposed. In CTPEM, the Visual Center Guided Enhancement (VCGE) module is proposed, which uses multiple visual centers to learn the binary relationship between pre- and post-temporal features to capture global and local information. Meanwhile, the Progressive Small Object Enhancement (PSOE) module is proposed, which is used to capture tiny and small-sized features through multiple feature maps at varying depths, aiming to reduce the influence of gradually vanishing features. Compared with state-of-the-art (SOTA) methods, CTPEM achieves superior performance in detecting damaged buildings, particularly small and tiny targets. Extensive experimental results demonstrate the effectiveness and practical value of our approach for accurate casualty assessment, and further facilitate more efficient disaster response and optimal resource allocation. The CTPEM code is available at https://github.com/GZPLHJ181107/CTPEM.