Deep Learning, Artificial Intelligence, Visual Language, Foundation Models, Vlm.
5-shot fine tuning, and outperforms full-set fine-tuning on the “Packages” dataset, consisting of only 26 images for training. 3.4. V+L Representation Learning VQA Compared with SimVLM, which uses 1.8B image-text pairs, Florence only uses 900M data to pre-train the image encoder and 20M for VLP,