The article presents a novel training paradigm, ITIT, for vision-language generative models. Current models rely on large corpora of paired image-text data for optimal performance. However, collecting such data leads…
Continue readingThe paper focuses on the Contrastive Language-Image Pre-training (CLIP) technique used in computer vision. The authors argue that the success of CLIP lies in its data, not its architecture or…
Continue readingT-MARS is a new data filtering approach designed to improve visual representations in large-scale image-text datasets. The method is motivated by the observation that a significant portion of images in…
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