Jan 5, 2023 · CiT contains two loops: an outer loop curating the training data and an inner loop consuming the curated training data. The text encoder ...

CiT contains two loops: an outer loop curating the training data and an inner loop consuming the curated training data. The text encoder connects the two loops.

Jan 5, 2023 · CiT automatically yields quality data to speed-up contrastive image-text training and alleviates the need for an offline data filtering pipeline ...

A simple and efficient vision-text learning algorithm that couples a data objective into training and can speed up training by over an order of magnitude.

CiT automatically yields quality data to speed-up contrastive image-text training and alleviates the need for an offline data filtering pipeline, allowing broad ...

CiT contains two loops: an outer loop curating the training data and an inner loop consuming the curated training data. The text encoder connects the two loops.

CiT contains two loops: an outer loop curating the training data and an inner loop consuming the curated training data. The text encoder connects the two loops.

Jan 5, 2023 · 1. Introduction Vision-language models have demonstrated success for fine-tuning and zero-shot transfer to downstream tasks [12, 21, 26] by ...

Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks by scaling up the dataset with image- ...

Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same ...