Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Shilong Liu*, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang* ;

"In this paper, we develop an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring expressions. The key solution of open-set object detection is introducing language to a closed-set detector for open-set concept generalization. To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for modalities fusion. We first pre-train Grounding DINO on large-scale datasets, including object detection data, grounding data, and caption data, and evaluate the model on both open-set object detection and referring object detection benchmarks. Grounding DINO performs remarkably well on all three settings, including benchmarks on COCO, LVIS, ODinW, and RefCOCO/+/g. Grounding DINO achieves a 52.5 AP on the COCO zero-shot1 detection benchmark. It sets a new record on the ODinW zero-shot benchmark with a mean 26.1 AP. We release some checkpoints and inference codes at https://github.com/ IDEA-Research/GroundingDINO. 1 In this paper, ‘zero-shot’ refers to scenarios where the training split of the test dataset is not utilized in the training process."