There are also a large number of videos that have audio channels describing what happens in these videos, and this audio can be transcribed into text as labels as well. These texts can be used as a “free” source of labels. There are tons of images on the web and social media that have annotated texts. The cross-modal representations of the pretrained models can then be finetuned to adapt to various downstream vision-language tasks.

The most representative approach is to train large transformer-based models on massive image-text pair data in a self-supervised manner, such as predicting the masked elements based on their context. Recently, Vision-Language Pre-training (VLP) has shown great progress toward addressing this problem. Motivated by the strong demand from real applications and recent research progresses on computer vision, natural language processing, and vision-language understanding, we strive to advance the state of the art on vision-language modeling, and develop the best computer vision technologies as part of our mission to empower everyone on the planet to achieve more. Florence-VL, as part of Project Florence, is funded by the Microsoft AI Cognitive Service team since 2020. For example, computers could mimic this ability by searching the most similar images for a text query (or vice versa) and by describing the content of an image using natural language.Īzure Florence-Vision and Language, short for Florence-VL, is launched to achieve this goal, where we aim to build new foundation models for Multimodal Intelligence. This data is similar to sights and sounds attained from vision and language that help humans make sense of the world around us. One of the core aspirations in artificial intelligence is to develop algorithms that endow computers with an ability to effectively learn from multi-modality (or multi-channel) data. Though any individual channel might be incomplete or noisy, humans can naturally align and fuse information collected from multiple channels, in order to grasp the key concepts needed for a better understanding of the world. Humans perceive the world through many channels, such as images viewed by the eyes or voices heard by the ears.

Transformer en vivo