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 …

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated
models like OpenAI's ChatGPT, represents a significant advancement in artificial …

We introduce VisoGender, a novel dataset for benchmarking gender bias in vision-language
models. We focus on occupation-related biases within a hegemonic system of binary …

A key goal for the advancement of AI is to develop technologies that serve the needs not just
of one group but of all communities regardless of their geographical region. In fact, a …

In this paper, we introduce Cross-View Language Modeling, a simple and effective pre-
training framework that unifies cross-lingual and cross-modal pre-training with shared …

Vision-language models (VLMs) have recently shown promising results in traditional
downstream tasks. Evaluation studies have emerged to assess their abilities, with the …

Recent advances in vision-language pre-training have pushed the state-of-the-art on
various vision-language tasks, making machines more capable of multi-modal writing …

Vision-language foundation models have had considerable increase in performances in the
last few years. However, there is still a lack of comprehensive evaluation methods able to …

Vision-language models (VLMs) have recently shown promising results in traditional
downstream tasks. Evaluation studies have emerged to assess their abilities with the …

Vision-language (VL) models, pretrained on colossal image-text datasets, have attained
broad VL competence that is difficult to evaluate. A common belief is that a small number of …