mlsanityData to 7 Oct 2026

Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

arXiv 2405.10300 ↗First posted 16 May 2024cs.CVcited by 122

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Cited by 27 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detection. The suite encompasses two models: Grounding DINO 1.5 Pro, a high-performance model designed for stronger generalization capability across a wide range of scenarios, and Grounding DINO 1.5 Edge, an efficient model optimized for faster speed demanded in many applications requiring edge deployment. The Grounding DINO 1.5 Pro model advances its predecessor by scaling up the model architecture, integrating an enhanced vision backbone, and expanding the training dataset to over 20 million images with grounding annotations, thereby achieving a richer semantic understanding. The Grounding DINO 1.5 Edge model, while designed for efficiency with reduced feature scales, maintains robust detection capabilities by being trained on the same comprehensive dataset. Empirical results demonstrate the effectiveness of Grounding DINO 1.5, with the Grounding DINO 1.5 Pro model attaining a 54.3 AP on the COCO detection benchmark and a 55.7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection. Furthermore, the Grounding DINO 1.5 Edge model, when optimized with TensorRT, achieves a speed of 75.2 FPS while attaining a zero-shot performance of 36.2 AP on the LVIS-minival benchmark, making it more suitable for edge computing scenarios. Model examples and demos with API will be released at https://github.com/IDEA-Research/Grounding-DINO-1.5-API

Key facts. Cited by 122 papers in the map (data through 7 Oct 2026). Builds on 34 of its 37 references that are in the map. First posted on arXiv 16 May 2024.

Builds on

34 of 37 references.

References · 34

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  9. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
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22 more
  1. 2021Florence: A New Foundation Model for Computer Visioncited by 802
  2. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 526
  3. 2024YOLO-World: Real-Time Open-Vocabulary Object Detectioncited by 498
  4. 2023EVA-02: A Visual Representation for Neon Genesiscited by 377
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  6. 2022Simple Open-Vocabulary Object Detection with Vision Transformerscited by 303
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  8. 2023A Simple Framework for Open-Vocabulary Segmentation and Detectioncited by 197
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  12. 2023DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignmentcited by 90
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  15. 2023Detecting Everything in the Open World: Towards Universal Object Detectioncited by 62
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  17. 2021Evaluating Large-Vocabulary Object Detectors: The Devil is in the Detailscited by 57
  18. 2023Visual In-Context Promptingcited by 52
  19. 2024DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detectioncited by 48
  20. 2023Multi-modal Queried Object Detection in the Wildcited by 47
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 17; 2025: 59; 2026: 46.

122 papers.

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