In recent years, multimedia computing techniques have demonstrated remarkable advancements in various domains, encompassing computer vision, natural language processing, and speech recognition.However, current multimedia computing models often face difficulties in expanding their capabilities when confronted with constraints such as limited data storage, limited reliable labels, and limited computational power. This struggle hinders the practical implementation of multimedia models in real-world scenarios, where the efficient computing under limited sources is crucial. Recognizing this necessity, our workshop aims to explore the complex realm of efficient learning ability of multimedia model, with the goal of advancing practical real-world applications.

This workshop centers on the academic exploration of efficient methodologies within the realm of multimedia computing. Our focus includes data-efficient strategies like image/video-compression, label-efficient strategies like zero/few-shot learning, as well as modelefficient approaches like model sparsification/quantization. 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 efficient multimedia computation. The importance of this workshop is emphasized by the current relevance of its theme, which has received considerable attention from researchers because of its direct relevance to practical uses. Through this platform, we aim to facilitate the sharing of ideas, creating a space for the expression of innovative perspectives aimed at tackling the efficiency issues faced by multimedia computing. This endeavor aims to contribute to the progress of real-world applications.