12 datasets found

MiniGPT-v2 is a vision-language model that uses a unified interface for multi-task learning.

Perceptual grouping in contrastive vision-language models.

UniDiff is a unified vision-language model that integrates discriminative and generative capabilities in vision-language tasks.

DataComp-10M is used as a pretraining dataset

CC3M and CC12M are used as datasets for training and evaluation

Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid...

The dataset used in the paper is Conceptual Captions 12M (CC12M) and RedCaps.

The dataset used in the paper is Conceptual Captions 3M (CC3M), Conceptual Captions 12M (CC12M), RedCaps, and LAION-400M.

A method for learning to prompt for vision-language models.

Conceptual Captions (CC-3M) is a large-scale dataset of 300,000 image-caption pairs.

CC-3M is a large-scale dataset of 300,000 image-caption pairs.

CC-Neg is a large-scale dataset of 228, 246 image-caption pairs along with high quality negated captions as distractors.