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About this paper
Xiangyu Zhao, Yicheng Chen, Shilin Xu, Xiangtai Li, Xinjiang Wang, Yining Li, Haian Huang
MM-Grounding-DINO, an open-source pipeline from Shanghai AI Lab and SenseTime Research, reconstructs and extends Grounding-DINO within the MMDetection framework, providing a unified solution for object grounding and detection tasks. The system leverages extensive public datasets and achieves improved performance, notably a +12.6 AP gain on LVIS MiniVal and strong transferability across diverse downstream applications.
Key insights
- Providing an open-source, comprehensive pipeline significantly democratizes state-of-the-art multi-modal vision technology, fostering reproducibility and extensibility for the research community.
- Careful selection and integration of large-scale public datasets, particularly those with vast vocabularies like V3Det, are critical for improving zero-shot transferability and performance on open-vocabulary benchmarks.
- The MM-Grounding-DINO framework exhibits robust generalizability and effectively adapts to diverse real-world downstream tasks through fine-tuning, underscoring its practical utility as a foundation model.
Summary by alphaXiv.
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