Many types of computer vision tasks possible with new customizable vision foundation model, Florence

Many types of computer vision tasks possible with new customizable vision foundation model, Florence

Florence: A New Foundation Model for Computer Vision

arXiv paper abstract https://arxiv.org/abs/2111.11432

arXiv PDF paper https://arxiv.org/pdf/2111.11432.pdf

... understanding ... diverse ... world demands computer vision models to generalize well with minimal customization for specific tasks

... vision foundation models ... such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation,

... new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth).

... incorporating ... Web-scale image-text data ... model ... easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition.

... outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects.

... achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.

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