In the last year alone, a surge of new benchmarks to measure $\textit {compositional} $
understanding of vision-language models have permeated the machine learning ecosystem …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

The combination of strong visual backbones and Large Language Model (LLM) reasoning
has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range …

Large Vision-Language Models (LVLMs) show significant strides in general-purpose
multimodal applications such as visual dialogue and embodied navigation. However …

Recent vision-language (VL) models are powerful, but can they reliably distinguish" right"
from" left"? We curate three new corpora to quantify model comprehension of such basic …

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in
a variety of tasks. However, some aspects of complex language understanding still remain a …

A fundamental characteristic common to both human vision and natural language is their
compositional nature. Yet despite the performance gains contributed by large vision and …

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 …

Large-scale pre-trained Vision & Language (VL) models have shown remarkable
performance in many applications, enabling replacing a fixed set of supported classes with …

Abstract Large scale Vision-Language (VL) models have shown tremendous success in
aligning representations between visual and text modalities. This enables remarkable …