Abstract Visual Question Answering (VQA) is a challenging task that has received increasing
attention from both the computer vision and the natural language processing communities …

The multimodal task of Visual Question Answering (VQA) encompassing elements of
Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers …

Inspired by the superior language abilities of large language models (LLM), large vision-
language models (LVLM) have been recently explored by integrating powerful LLMs for …

Large language models (LLMs) and Vision-Language Models (VLMs) have been proved to
excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be …

When answering a question, humans utilize the information available across different
modalities to synthesize a consistent and complete chain of thought (CoT). This process is …

In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to
answer questions regarding different visual objects, sounds, and their associations in …

This paper presents a unified Vision-Language Pre-training (VLP) model. The model is
unified in that (1) it can be fine-tuned for either vision-language generation (eg, image …

Recent VQA models may tend to rely on language bias as a shortcut and thus fail to
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …

We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …

Recent advancements in GPT-4V have displayed remarkable multi-modal capabilities in
processing image inputs and following open-ended instructions. Despite these …