Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP (Radford et al., 2021), ALIGN (Jia et al., 2021), and Wu Dao 2.0 (Wud) focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a 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). By incorporating universal visual-language representations from Web-scale image-text data, our Florence model can be easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition. Moreover,
These data demonstrate that raloxifene has vascular relaxing properties. The surprising finding is that the receptor-dependent effects via the endothelium are observed in coronary arteries from both male and female animals.
This study demonstrates that phytoestrogens induce endothelium-independent relaxation of coronary arteries; the mechanism involves calcium antagonism. These mechanisms may contribute to the potential long-term cardiovascular protective effect of these substances.
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