Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

• URL: http://arxiv.org/abs/2311.06242v1
• Date: Fri, 10 Nov 2023 18:59:08 GMT
• Title: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
• Authors: Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, Lu Yuan
• Abstract summary: 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. We co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks.
• Score: 94.49801814314435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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