# Hierarchical Contextual Grounding LVLM: Enhancing Fine-Grained Visual-Language Understanding with Robust Grounding

arXiv: https://arxiv.org/abs/2508.16974 · HTML: https://mlsanity.com/paper/2508.16974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 23 of 23)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models](https://mlsanity.com/paper/2312.12730/) (2023)
- [Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models](https://mlsanity.com/paper/2406.16866/) (2024)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback](https://mlsanity.com/paper/2501.01377/) (2025)
- [MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration](https://mlsanity.com/paper/2506.19835/) (2025)
- [HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding](https://mlsanity.com/paper/2404.13400/) (2024)
- [Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models](https://mlsanity.com/paper/2501.15140/) (2025)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate](https://mlsanity.com/paper/2410.07167/) (2024)
- [Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models](https://mlsanity.com/paper/2308.09778/) (2023)
- [Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models](https://mlsanity.com/paper/2505.17316/) (2025)
- [Quantized Prompt for Efficient Generalization of Vision-Language Models](https://mlsanity.com/paper/2407.10704/) (2024)
- [A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task](https://mlsanity.com/paper/2504.17547/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2509.25373/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) and Vision-Language Large Models (LVLMs) have achieved remarkable progress in natural language processing and multimodal understanding. Despite their impressive generalization capabilities, current LVLMs often exhibit insufficient robustness, proneness to hallucination, and reasoning errors in complex real-world scenarios, particularly when precise image region localization and fine-grained visual reasoning are required. To address these limitations, we propose the Hierarchical Contextual Grounding LVLM (HCG-LVLM), a novel architecture that mimics human coarse-to-fine cognitive processing. HCG-LVLM employs a two-layered approach: a Global Contextual Perception layer for initial broad understanding and a Fine-grained Local Grounding layer. The latter incorporates a Local Detail Enhancement Module to extract high-resolution features and a Semantic Consistency Validator to ensure accurate, hallucination-free visual-language alignment. Through an adaptive fusion mechanism, information from both layers is integrated for robust and precise outputs. Extensive experiments on challenging datasets, including GQA, A-OKVQA for fine-grained VQA, and RefCOCO/+/g for Referring Expression Comprehension, demonstrate that HCG-LVLM consistently outperforms state-of-the-art models such as Flamingo, BLIP-2, and MiniGPT-4. Our model achieves superior accuracy and significantly reduces hallucination, validating the effectiveness of its hierarchical design in enhancing fine-grained visual-language understanding and precise grounding capabilities.