In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet
often constrained by the scarcity of high-quality image-text data. To address this bottleneck …

Large multimodal models demonstrate remarkable generalist ability to perform diverse
multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute …

Contrastive pre-training of image-text foundation models such as CLIP demonstrated
excellent zero-shot performance and improved robustness on a wide range of downstream …

The recent advance in vision-language models is largely attributed to the abundance of
image-text data. We aim to replicate this success for video-language models but there …

Data poisoning attacks manipulate training data to introduce unexpected behaviors into
machine learning models at training time. For text-to-image generative models with massive …

We train a set of open text-to-image (T2I) diffusion models on a dataset of curated Creative-
Commons-licensed (CC) images which yields models that are competitive with Stable …

Despite being (pre) trained on a massive amount of data state-of-the-art video-language
alignment models are not robust to semantically-plausible contrastive changes in the video …

Abstract Vision-Language Models (VLMs) are pretrained on large diverse and noisy web-
crawled datasets. This underscores the critical need for dataset pruning as the quality of …

Web-crawled datasets are pivotal to the success of pre-training vision-language models,
exemplified by CLIP. However, web-crawled AltTexts can be noisy and potentially irrelevant …

Localizing and recognizing objects in the open-ended physical world poses a long-standing
challenge within the domain of machine perception. Recent methods have endeavored to …