Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …

Large Vision-Language Models (LVLMs) have recently played a dominant role in
multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation …

Autonomous driving is an active research topic in both academia and industry. However,
most of the existing solutions focus on improving the accuracy by training learnable models …

Vision-language pre-training (VLP) has shown impressive performance on a wide range of
cross-modal tasks, where VLP models without reliance on object detectors are becoming the …

Abstract Explanatory Visual Question Answering (EVQA) is a recently proposed multimodal
reasoning task that requires answering visual questions and generating multimodal …

Abstract Medical Vision-and-Language Pre-training (MedVLP), which learns generic vision-
language representations from medical images and texts to benefit various downstream …

Visual Question Answering (VQA) is a challenging task that requires systems to provide
accurate answers to questions based on image content. Current VQA models struggle with …

Visual reasoning is dominated by end-to-end neural networks scaled to billions of model
parameters and training examples. However, even the largest models struggle with …

The intersection of vision and language is of major interest due to the increased focus on
seamless integration between recognition and reasoning. Scene graphs (SGs) have …

When faced with an out-of-distribution (OOD) question or image, visual question answering
(VQA) systems may provide unreliable answers. If relied on by real users or secondary …