The LVM's scalability across model and data size is demonstrated, with larger LVMs performing better on downstream tasks. The model's performance is evaluated on different sub-components of datasets, showing that it benefits from each single image, video, and annotation. The article also presents results of the LVM predicting the next frame in video sequences, handling in and out of distribution prompting examples, and compositing multiple tasks within a single prompt. The model is also shown to be effective in handling tasks that are not easily describable in language, non-verbal IQ tests, and a variety of simple vision tasks.
Key takeaways:
- The researchers introduced a novel sequential modeling approach, Large Vision Model (LVM), that does not require any linguistic data. Instead, it uses a common format called "visual sentences" to represent raw images, videos, and annotated data sources.
- The LVM is trained on a wide variety of visual data (420 billion tokens) to minimize cross-entropy loss for next token prediction. The model has shown effective scalability across various scales of model architecture and data diversity.
- The LVM can perform various vision tasks by designing suitable prompts at test time. It can predict the next frame in a video, handle out-of-distribution prompting examples, and composite several tasks within a single prompt.
- The LVM can also handle tasks that are not easily describable in language, non-verbal IQ tests, and a variety of simple vision tasks such as object replication, relighting, and zooming in.