Contemporary large-scale visual language models (VLMs) exhibit strong representation
capacities, making them ubiquitous for enhancing the image and text understanding tasks …

Vision-language models (VLMs) are impactful in part because they can be applied to a
variety of visual understanding tasks in a zero-shot fashion, without any fine-tuning. We …

Abstract Recent advancements in Vision-Language Models (VLMs) have marked a
significant leap in bridging the gap between computer vision and natural language …

Geometric understanding is crucial for navigating and interacting with our environment.
While large Vision Language Models (VLMs) demonstrate impressive capabilities …

Large Language Models (LLMs) have been successfully used in many natural-language
tasks and applications including text generation and AI chatbots. They also are a promising …

Despite their remarkable successes, state-of-the-art language models face challenges in
grasping certain important semantic details. This paper introduces the VISLA (Variance and …

Despite their remarkable successes, state-of-the-art large language models (LLMs),
including vision-and-language models (VLMs) and unimodal language models (ULMs), fail …

We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of
large vision-language models on comprehension tasks. Unlike current benchmarks that …

Recent works often assume that Vision-Language Model (VLM) representations are based
on visual attributes like shape. However, it is unclear to what extent VLMs prioritize this …

Visual Language Models have exhibited impressive performance on new tasks in a zero-
shot setting. Language queries enable these large models to classify or detect objects even …