arxiv preprint - 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities

In this episode, we discuss 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities by Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, Amir Zamir. The paper presents a novel any-to-any model that significantly extends the capabilities of existing multimodal and multitask foundation models by training on tens of highly diverse modalities, including images, text, geometric data, and more. Through discrete tokenization of various data types and co-training on large-scale datasets, the model can address three times more tasks/modalities than current models without sacrificing performance. The authors demonstrate this with a three billion parameter model, providing open access to the models and training code.