The capacity for flexible and efficient generalization from limited resources is a hallmark of human intelligence. However, artificial intelligence (AI) models often encounter challenges in extending their capabilities from limited resources, such as constrained training data and computation, particularly in fields such as computer vision and natural language processing. The struggle to generalize effectively from limited resources hinders the practical deployment of AI models in the open world, where efficient learning from sparse samples in unpredictable scenarios is essential. Recognizing this imperative, our workshop aims to delve into the intricate landscape of AI model generalization in open-world settings to foster advancements in real-world AI applications. Numerous endeavors have been undertaken to surmount these challenges, addressing the inefficiencies in learning models and bridging the divide between human and machine intelligence.

This workshop centers on the academic exploration of efficient methodologies within the realm of artificial intelligence (AI) models. Our focus encompasses both data-efficient strategies, such as zero/few-shot learning and domain adaptation, as well as model-efficient approaches like model sparsification and compact model design. By convening researchers specializing in these areas, our objective is to facilitate the sharing of recent research findings and engage in discussions about the future trajectories of AI model generalization. The significance of this workshop is underscored by the timely relevance of its thematic focus, which has garnered substantial attention from the research community due to its direct implications for practical applications. Through this forum, we aspire to foster the exchange of ideas, providing an avenue for the articulation of novel insights aimed at addressing the generalization challenges confronting AI models, thereby contributing to the advancement of real-world AI applications.