Microsoft Releases Florence-2: A Novel Vision Foundation Model
A Unified, Prompt-Based Representation for Computer Vision and Vision-Language Tasks
There has been a notable shift in AGI systems towards using pretrained, adaptable representations known for their task-agnostic benefits in various applications. The success of natural language processing has inspired a similar strategy in computer vision.
A team of Microsoft researchers introduces Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks. This model solves the problems of needing a consistent architecture and limiting comprehensive data by creating a single, prompt-based representation for all vision activities.
Using FLD-5B, the data engine generates a complete visual dataset with a total of 5.4B annotations for 126M images—a significant improvement over labor-intensive manual annotation. The engine’s two processing modules are highly efficient.
The Florence-2 model stands out for its unique features. It integrates an image encoder and a multi-modality encoder-decoder into a sequence-to-sequence (seq2seq) architecture, following the NLP community’s goal of developing flexible models with a consistent framework.
After fine-tuning using publicly available human-annotated data, Florence-2 achieves new state-of-the-art performances on the benchmarks on RefCOCO/+/g. This pre-trained model outperforms supervised and self-supervised models on downstream tasks, including ADE20K semantic segmentation and COCO object detection and instance segmentation.
If you want to evolve your company with AI, stay competitive, use for your advantage Microsoft Releases Florence-2: A Novel Vision Foundation Model with a Unified, Prompt-based Representation for a Variety of Computer Vision and Vision-Language Tasks.
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