Learning Generalized Knowledge for AI with Limited Supervision

Abstract: As babies, we begin to grasp the world through spontaneous observations, gradually developing generalized knowledge about the world. This foundational knowledge enables humans to effortlessly learn new skills without extensive teaching for each task. Can we develop a similar paradigm for AI? This talk describes how learning from limited supervision can address fundamental challenges in AI such as scalability and generalization while embedding generalized knowledge. I will first talk about our research in self-supervised learning, utilizing natural images and videos without human-annotated labels. By deriving supervisory signals from the data itself, our self-supervised models achieve scalable and universal representations. The second part will describe how to leverage non-curated image-text pairs, through which we obtain textual representation of images. This representation comprehensively describes semantic elements in an image and bridges various AI tools such as large language models (LLMs), enabling diverse vision-language applications. Collectively, this talk argues the benefits of harnessing limited supervision for developing more general, adaptable, and efficient AI, which is ultimately better able to serve human needs.

Bio: Chen Wei is a PhD candidate at the Computer Science Department of Johns Hopkins University, advised by Alan Yuille. Her research in Artificial Intelligence, Machine Learning, and Computer Vision focuses on developing AI systems that generalize to a wide range of novel tasks and are adaptable to new environments. Her research involves Self-Supervised Learning, Generative Modeling, and Vision-Language Understanding. She has published at many top-tier CV and AI venues. Chen is a recipient of EECS Rising Star in 2023 and ECCV Outstanding Reviewer. During her PhD, Chen was a research intern at FAIR at Meta AI and Google DeepMind. Before Johns Hopkins, she obtained her BS in Computer Science from Peking University.

Website: https://weichen582.github.io/