Fine-grained multimodal capability in Multimodal Large Language Models (MLLMs) has emerged as a critical research direction, particularly for tackling the visual grounding (VG) problem. Despite the strong performance achieved by existing approaches, they often employ disparate design choices when fine-tuning MLLMs for VG, lacking systematic verification to support these designs. To bridge this gap, this paper presents a comprehensive study of various design choices that impact the VG performance of MLLMs. We conduct our analysis using LLaVA-1.5, which has been widely adopted in prior empirical studies of MLLMs. While more recent models exist, we follow this convention to ensure our findings remain broadly applicable and extendable to other architectures. We cover two key aspects: (1) exploring different visual grounding paradigms in MLLMs, identifying the most effective design, and providing our insights; and (2) conducting ablation studies on the design of grounding data to optimize MLLMs' fine-tuning for the VG task. Finally, our findings contribute to a stronger MLLM for VG, achieving improvements of +5.6% / +6.9% / +7.0% on RefCOCO/+/g over the LLaVA-1.5.
Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue
Hunan Police Academy, Department of Criminal Investigation（湖南警察学院犯罪侦查系）；Business College, California State University（加州州立大学长滩分校商学院）；Northwestern University（西北大学）；AI Agent Lab, Vokram Group（Vokram集团AI代理实验室）；Beijing University of Technology（北京理工大学）；Zheng Zhou University of Light Industry（郑州轻工业大学）
Trifuse: Enhancing Attention-Based GUI Grounding via Multimodal Fusion
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Intelligent Game and Decision Lab, Academy of Military Sciences（智能游戏与决策实验室，军事科学院）
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge
Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究院）
