This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

We propose a novel multimodal video benchmark-the Perception Test-to evaluate the
perception and reasoning skills of pre-trained multimodal models (eg Flamingo, BEiT-3, or …

Abstract While Visual Question Answering (VQA) has progressed rapidly, previous works
raise concerns about robustness of current VQA models. In this work, we study the …

High-quality visualization collections are beneficial for a variety of applications including
visualization reference and data-driven visualization design. The visualization community …

Spatial understanding is a fundamental aspect of computer vision and integral for human-
level reasoning about images, making it an important component for grounded language …

Abstract Visual Question Answering (VQA) models often perform poorly on out-of-distribution
data and struggle on domain generalization. Due to the multi-modal nature of this task …

While progress has been made on the visual question answering leaderboards, models
often utilize spurious correlations and priors in datasets under the iid setting. As such …

We introduce an evaluation methodology for visual question answering (VQA) to better
diagnose cases of shortcut learning. These cases happen when a model exploits spurious …

Abstract Despite advances in Visual Question Answering (VQA), the ability of models to
assess their own correctness remains underexplored. Recent work has shown that VQA …

Object hallucination poses a significant challenge in vision-language (VL) models, often
leading to the generation of nonsensical or unfaithful responses with non-existent objects …