Autonomous driving is an emerging technology that has advanced rapidly over the last
decade. Modern transportation is expected to benefit greatly from a wise decision-making …

Unlike humans, who can effortlessly estimate the entirety of objects even when partially
occluded, modern computer vision algorithms still find this aspect extremely challenging …

Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual
capability in answering visual questions, however, little is known about the limits of their …

In the era of advanced multimodel learning, multimodal large language models (MLLMs)
such as GPT-4V have made remarkable strides towards bridging language and visual …

Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a
plethora of noteworthy contributions in recent months. The prevailing trend involves …

Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …

Abstract Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities
in various multi-modal tasks. Nevertheless their performance in fine-grained image …

Recent advancements in Multimodal Large Language Models (MLLMs) underscore the
significance of scalable models and data to boost performance, yet this often incurs …

Large Language Models (LLMs) are capable of reasoning over diverse input data modalities
through pre-trained encoders. However, the growing diversity of input data modalities …

Accurate scene understanding from multiple sensors mounted on cars is a key requirement
for autonomous driving systems. Nowadays, this task is mainly performed through data …