Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

This paper presents contrastive-tuning, a simple method employing contrastive training to
align image and text models while still taking advantage of their pre-training. In our empirical …

Abstract Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D
zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP …

This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a …

A variety of attention mechanisms have been studied to improve the performance of various
computer vision tasks. However, the prior methods overlooked the significance of retaining …

Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …

Generating shapes using natural language can enable new ways of imagining and creating
the things around us. While significant recent progress has been made in text-to-image …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

In recent years, the growing number of medical imaging studies is placing an ever-
increasing burden on radiologists. Deep learning provides a promising solution for …