A new model, MM-Grounding-DINO, is proposed by Shanghai AI Lab and SenseTime Research for unified object grounding and detection tasks. This user-friendly and open-source pipeline outperforms existing models in various domains, achieving state-of-the-art performance and setting new benchmarks for mean average precision (mAP). The study introduces a comprehensive evaluation framework for diverse datasets.
MM-Grounding-DINO: An Innovative AI Solution for Unified Object Grounding and Detection
Researchers from Shanghai AI Lab and SenseTime Research have developed MM-Grounding-DINO, an open and user-friendly pipeline aimed at revolutionizing object detection. This innovative solution builds upon the foundation of Grounding-DINO and addresses critical tasks such as OVD, PG, and REC.
Features and Benefits
MM-Grounding-DINO utilizes a comprehensive pipeline, leveraging diverse vision datasets for pre-training and fine-tuning with detection and grounding datasets. Through extensive benchmark experiments, MM-Grounding-DINO demonstrates state-of-the-art performance across various domains, surpassing existing benchmarks for mean average precision (mAP).
How It Works
When presented with an image-text pair, MM-Grounding-DINO employs both image and text backbones to extract features, followed by a thorough fusion of image and text features. This innovative approach facilitates precise object grounding and detection, setting new standards for performance and accuracy.
Practical Implementation
For companies looking to evolve with AI, MM-Grounding-DINO offers an opportunity to redefine work processes and automate customer engagement. By identifying automation opportunities, defining KPIs, and selecting the right AI solution, businesses can gradually implement AI to achieve measurable impacts on business outcomes.
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