Visible-thermal cross-modality learning enhances downstream task performance by integrating information from multiple sources. In real-world scenarios such as autonomous driving, new classes continually emerge, and data is often incomplete due to sensor occlusions. This raises a key question about how to incrementally update a model with incomplete cross-modality data. To address this problem, we propose a practical task termed incomplete cross-modality class-incremental learning (ICMCIL), which aims to effectively leverage incomplete cross-modality information to learn new knowledge without forgetting the old. We construct a benchmark for ICMCIL and thoroughly analyze its challenges, revealing that (1) different modalities experience varying degrees of forgetting, (2) conventional cross-modality fusion only partially alleviates forgetting, and (3) missing data exacerbates the forgetting of previous classes. To address these issues, we propose Hybrid Fusion via Completion (HFC), a unified framework that integrates completion, fusion, and forgetting prevention. Additionally, we enhance information fusion by introducing a feature interchange mechanism, wherein features are shuffled and channels are reordered to improve information flow. Extensive experiments demonstrate that HFC effectively addresses ICMCIL, significantly mitigating modality forgetting.

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.