Zhiyuan Research Institute recently released a new generation of multi-modal basic model Emu2, which has made significant breakthroughs in multi-modal context learning capabilities. Through large-scale autoregressive generative multi-modal pre-training, Emu2 performs well in few-sample multi-modal understanding tasks, surpassing mainstream models such as Flamingo-80B and IDEFICS-80B, and has performed well in multiple few-sample understanding, visual question answering and Achieve optimal performance on image generation tasks. Emu2 contains two main applications: Emu2-Chat and Emu2-Gen, which focus on image and text instruction understanding and image/video generation respectively.
Zhiyuan Research Institute has released a new generation of multi-modal basic model Emu2, which significantly promotes breakthroughs in multi-modal context learning capabilities through large-scale autoregressive generative multi-modal pre-training. Emu2 performs well on few-sample multi-modal understanding tasks, surpassing the mainstream multi-modal pre-trained large models Flamingo-80B and IDEFICS-80B. Emu2 has achieved optimal performance in multiple few-shot understanding, visual question answering, and image generation tasks. Emu2-Chat can accurately understand graphic and text instructions to achieve better information perception, intention understanding and decision-making planning. Emu2-Gen can accept images, text, and interleaved position sequences as input to achieve flexible, controllable, and high-quality image and video generation. Emu2 adopts a simpler modeling framework and scales the model to 37B parameters. For details, please refer to the project link released by Zhiyuan Research Institute.With its powerful performance and concise framework, Emu2 demonstrates the latest progress in the field of multi-modal artificial intelligence and provides a solid foundation for the development of future multi-modal applications. The continuous innovation of Zhiyuan Research Institute is worth looking forward to.