Open-vocabulary models are a promising new paradigm for image classification. Unlike
traditional classification models, open-vocabulary models classify among any arbitrary set of …

Massive web datasets play a key role in the success of large vision-language models like
CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to …

Text-to-image generation models that generate images based on prompt descriptions have
attracted an increasing amount of attention during the past few months. Despite their …

Understanding verbs is crucial to modelling how people and objects interact with each other
and the environment through space and time. Recently, state-of-the-art video-language …

Abstract Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet
effective way to train large-scale vision-language models. CLIP demonstrates impressive …

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE\cdot2 are
revolutionizing how people generate visual content. At the same time, society has serious …

The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

Vision and Language (VL) models offer an effective method for aligning representation
spaces of images and text allowing for numerous applications such as cross-modal retrieval …

Face anti-spoofing (FAS) or presentation attack detection is an essential component of face
recognition systems deployed in security-critical applications. Existing FAS methods have …

Images of the natural world collected by a variety of cameras from drones to individual
phones are increasingly abundant sources of biological information. There is an explosion …