Conversational generative AI has demonstrated remarkable promise for empowering
biomedical practitioners, but current investigations focus on unimodal text. Multimodal …

Large-scale text-to-image diffusion models have made amazing advances. However, the
status quo is to use text input alone, which can impede controllability. In this work, we …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

CLIP, one of the pioneering foundation models that connect images and text, has enabled
many recent breakthroughs in computer vision. However, its associated training cost is …

Vision-language models (VLMs) excel in zero-shot recognition but their performance varies
greatly across different visual concepts. For example although CLIP achieves impressive …

The field of natural language processing (NLP) has made significant strides in recent years,
particularly in the development of large-scale vision-language models (VLMs). These …

The recent surge of foundation models in computer vision and natural language processing
opens up perspectives in utilizing multi-modal clinical data to train large models with strong …

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision
community. The VLMs provide stronger and more generalizable feature embeddings …

Abstract We propose Neural Priming, a technique for adapting large pretrained models to
distribution shifts and downstream tasks given few or no labeled examples. Presented with …

Contrastive image-text models such as CLIP form the building blocks of many state-of-the-art
systems. While they excel at recognizing common generic concepts, they still struggle on …