Abstract Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability
to perceive and understand multi-modal signals. However most of the existing MLLMs …

The emergence of multimodal large models (MLMs) has significantly advanced the field of
visual understanding, offering remarkable capabilities in the realm of visual question …

Instruction tuning is an essential supervised training phase for Large Language Models
(LLMs), with the goal of enhancing LLMs' capacity to generalize instruction execution and …

Contemporary news reporting increasingly features multimedia content, motivating research
on multimedia event extraction. However, the task lacks annotated multimodal training data …

Large Language Models (LLMs), primarily trained on text-based datasets, exhibit
exceptional proficiencies in understanding and executing complex linguistic instructions via …

Traditional computer vision generally solves each single task independently by a dedicated
model with the task instruction implicitly designed in the model architecture, arising two …

While Large Vision-Language Models (LVLMs) have exhibited remarkable capabilities
across a wide range of tasks, they suffer from hallucination problems, where models …

Long video understanding is a significant and ongoing challenge in the intersection of
multimedia and artificial intelligence. Employing large language models (LLMs) for …

From content moderation to wildlife conservation the number of applications that require
models to recognize nuanced or subjective visual concepts is growing. Traditionally …

Medical artificial general intelligence (AGI) is an emerging field that aims to develop systems
specifically designed for medical applications that possess the ability to understand, learn …