Object Detection with Multimodal Large Vision-Language Models: An In-depth Review

2025-08-25

AI Summary

Conventional object detection models exhibit limitations in contextual reasoning, cross-scene generalization, and fine-grained localization. Method: This work systematically surveys recent advances in leveraging Large Vision-Language Models (LVLMs) for object detection, proposing a three-stage analytical framework—pretraining, alignment, and fine-tuning—to dissect multimodal fusion mechanisms, cross-modal alignment strategies, and end-to-end training paradigms. Empirical evaluation is conducted via visualization analysis and multi-benchmark comparisons across detection and segmentation tasks. Contribution/Results: LVLMs substantially enhance semantic understanding and few-shot generalization in complex scenes, achieving superior localization accuracy and contextual consistency compared to classical methods. The study provides theoretical foundations and technical pathways toward interpretable, promptable, and scalable next-generation detection frameworks.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. This review presents comprehensive visualizations demonstrating LVLMs' effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. The review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and robotic applications in the future.

Problem

• Enhancing object detection through multimodal vision-language fusion
• Addressing limitations of traditional deep learning architectures
• Improving contextual reasoning and generalization in object recognition

Innovation

• Fusion of language and vision techniques
• Architectural innovations and training paradigms
• Integration of visual and textual information