理解视觉语言模型中的视觉定位
Towards Understanding Visual Grounding in Visual Language Models
AI总结：
本综述回顾了现代通用视觉语言模型（VLMs）中视觉定位的研究，系统梳理了其核心组件、实际应用基准、与多模态思维链及推理的相互关系，并分析了当前挑战与未来方向。
摘要：
视觉定位是指模型在某种视觉输入中识别出与文本描述相匹配区域的能力。因此，具备视觉定位能力的模型可以广泛应用于各个领域的多种任务，包括指代表达理解、回答与图像或视频中细粒度细节相关的问题、通过明确提及实体为视觉上下文生成描述，以及在模拟和真实环境中进行低级和高级控制。在这篇综述论文中，我们回顾了现代通用视觉语言模型（VLMs）研究领域关键方向的代表性工作。我们首先概述了定位在VLMs中的重要性，然后描绘了当代开发定位模型范式的核心组件，并考察了它们的实际应用，包括用于定位多模态生成的基准和评估指标。我们还讨论了VLMs中视觉定位、多模态思维链与推理之间的多方面相互关系。最后，我们分析了视觉定位固有的挑战，并为未来的研究提出了有前景的方向。
英文摘要：
Visual grounding refers to the ability of a model to identify a region within some visual input that matches a textual description. Consequently, a model equipped with visual grounding capabilities can target a wide range of applications in various domains, including referring expression comprehension, answering questions pertinent to fine-grained details in images or videos, caption visual context by explicitly referring to entities, as well as low and high-level control in simulated and real environments. In this survey paper, we review representative works across the key areas of research on modern general-purpose vision language models (VLMs). We first outline the importance of grounding in VLMs, then delineate the core components of the contemporary paradigm for developing grounded models, and examine their practical applications, including benchmarks and evaluation metrics for grounded multimodal generation. We also discuss the multifaceted interrelations among visual grounding, multimodal chain-of-thought, and reasoning in VLMs. Finally, we analyse the challenges inherent to visual grounding and suggest promising directions for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
