Florence-2: Advancing a Unified Representation for a Variety of Vision
  Tasks
Bin Xiao Haiping Wu Weijian Xu Xiyang Dai Houdong Hu Yumao Lu Michael Zeng Ce Liu Lu Yuan
We introduce Florence-2, a novel vision foundation model with a unified,
prompt-based representation for a variety of computer vision and
vision-language tasks. While existing large vision models excel in transfer
learning, they struggle to perform a diversity of tasks with simple
instructions, a capability that implies handling the complexity of various
spatial hierarchy and semantic granularity. Florence-2 was designed to take
text-prompt as task instructions and generate desirable results in text forms,
whether it be captioning, object detection, grounding or segmentation. This
multi-task learning setup demands large-scale, high-quality annotated data. To
this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive
visual annotations on 126 million images, using an iterative strategy of
automated image annotation and model refinement. We adopted a
sequence-to-sequence structure to train Florence-2 to perform versatile and
comprehensive vision tasks. Extensive evaluations on numerous tasks
demonstrated Florence-2 to be a strong vision foundation model contender with
unprecedented zero-shot and fine-tuning capabilities.