Curriculum Learning for Multimedia in the Era of Large Language Models

摘要

This tutorial focuses on curriculum learning (CL), an important topic in machine learning, which gains an increasing amount of attention in the research community. CL is a learning paradigm that enables machines to learn from easy data to hard data, imitating the meaningful procedure of human learning with curricula. As an easy-to-use plug-in, CL has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision, natural language processing, reinforcement learning, etc. In particular, CL can also play an important role in multimedia applications. Therefore, it is essential to introduce CL to more scholars and researchers in the machine learning and multimedia community. However, there have been no tutorials on CL for multimedia so far, motivating the organization of this tutorial at ACM Multimedia 2024. To give a comprehensive tutorial on CL for multimedia, we plan to organize it from the following aspects: (1) theories, (2) approaches, (3) applications, (4) tools, and (5) future directions. First, we introduce the motivations, theories, and insights behind CL. Second, we advocate novel, high-quality approaches, as well as innovative solutions to the challenging problems in CL. Then we present the applications of CL in various scenarios, especially multimedia, followed by some relevant tools. In the end, we discuss open questions and future directions in the era of large language models. We believe this topic is at the core of the scope of ACM Multimedia and is attractive to the audience interested in machine learning and multimedia from both academia and industry.