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An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

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arxiv 2401.02361 v2 pith:YEMGA3H7 submitted 2024-01-04 cs.CV

Xiangyu Zhao , Yicheng Chen , Shilin Xu , Xiangtai Li , Xinjiang Wang , Yining Li , Haian Huang This is my paper · ORCID
classification cs.CV
keywords comprehensivedetectiongroundingbaselinedatasetsgrounding-dinommdetectionmodel
verification ladder T0 review T1 audit T2 compute T3 formal
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Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness has led to its widespread adoption as a mainstream architecture for various downstream applications. However, despite its significance, the original Grounding-DINO model lacks comprehensive public technical details due to the unavailability of its training code. To bridge this gap, we present MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline, which is built with the MMDetection toolbox. It adopts abundant vision datasets for pre-training and various detection and grounding datasets for fine-tuning. We give a comprehensive analysis of each reported result and detailed settings for reproduction. The extensive experiments on the benchmarks mentioned demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny baseline. We release all our models to the research community. Codes and trained models are released at https://github.com/open-mmlab/mmdetection/tree/main/configs/mm_grounding_dino.

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Forward citations

Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Global prompt integration, visual-textual relation distillation and selective fusion make visual prompts discriminative enough for DETR-ViP to beat prior visual-prompt detectors by several mAP points.

  2. 3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection

    cs.CV 2025-07 conditional novelty 7.0 of 10

    3D-MOOD is the first end-to-end monocular 3D object detector for open-set classes and novel scenes, achieving SOTA on Omni3D and on new open-set benchmarks.

  3. DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models

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    DINO-R1 trains visual-prompt detectors with group-relative query rewards and KL regularization, improving zero-shot and fine-tuned detection over supervised fine-tuning.

  4. Vision-Language Grounding as Bidirectional Concept Correspondence

    cs.CV 2026-08 conditional novelty 6.5 of 10

    Grounding can be treated as recovering all word-span to image-mask correspondences in one pass, and a bridge-token model, ConCor-1, does this better than existing grounding pipelines on the tested benchmarks.

  5. RegionDet: A Benchmark for Region Detection Beyond Object Instances

    cs.CV 2026-08 conditional novelty 6.0 of 10

    RegionDet shows supervised detectors can partially learn to localize activity and context regions, while open-vocabulary detectors nearly fail, indicating current vision-language models are object-centric.

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  12. UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time

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  13. DynamicEarth: How Far are We from Open-Vocabulary Change Detection?

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