[[{“value”:”*Equal Contributors
A dominant paradigm in large multimodal models is to pair a large language de- coder with a vision encoder. While it is well-known how to pre-train and tune language decoders for multimodal tasks, it is less clear how the vision encoder should be pre-trained. A de facto standard is to pre-train the vision encoder with a discriminative objective, such as contrastive loss. This causes a mismatch between pre-training and the generative autoregressive downstream task. At the same time, following their success in the language domain, autoregressive image models have been shown…”}]] [[{“value”:”*Equal Contributors
A dominant paradigm in large multimodal models is to pair a large language de- coder with a vision encoder. While it is well-known how to pre-train and tune language decoders for multimodal tasks, it is less clear how the vision encoder should be pre-trained. A de facto standard is to pre-train the vision encoder with a discriminative objective, such as contrastive loss. This causes a mismatch between pre-training and the generative autoregressive downstream task. At the same time, following their success in the language domain, autoregressive image models have been shown…”}]] Read More