Abstract Multi-modal Large Language Models (MLLMs) tuned on machine-generated
instruction-following data have demonstrated remarkable performance in various multimodal …

The issue of hallucinations is a prevalent concern in existing Large Vision-Language
Models (LVLMs). Previous efforts have primarily focused on investigating object …

Computer vision often treats perception as objective, and this assumption gets reflected in
the way that datasets are collected and models are trained. For instance, image descriptions …

In recent research significant attention has been devoted to the open-vocabulary object
detection task aiming to generalize beyond the limited number of classes labeled during …

Zero-shot image captioning (IC) without well-paired image-text data can be categorized into
two main types: training-free and text-only-training methods. While both types integrate pre …

The Large Visual Language Models (LVLMs) enhances user interaction and enriches user
experience by integrating visual modality on the basis of the Large Language Models …

Despite recent advances in the general visual instruction-following ability of Multimodal
Large Language Models (MLLMs), they still struggle with critical problems when required to …

Image captioning evaluation metrics can be divided into two categories, reference-based
metrics and reference-free metrics. However, reference-based approaches may struggle to …

Generating detailed captions comprehending text-rich visual content in images has received
growing attention for Large Vision-Language Models (LVLMs). However, few studies have …

Despite recent advances in the general visual instruction-following ability of Multimodal
Large Language Models (MLLMs), they still struggle with critical problems when required to …