In this paper titled "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection," authors Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu and Lei Zhang present a novel open-set object detector called Grounding DINO. This detector combines the Transformer-based detector DINO with grounded pre-training to detect arbitrary objects using human inputs such as category names or referring expressions. The key challenge in open-set object detection is generalizing concepts from closed-set detectors to handle open-set scenarios. To address this challenge and effectively fuse language and vision modalities, the authors propose a three-phase approach for conceptually dividing a closed-set detector. The proposed approach includes a feature enhancer, a language-guided query selection mechanism and a cross-modality decoder for cross-modality fusion. While previous works mainly focus on evaluating open set object detection on novel categories, the authors propose an additional evaluation on referring expression comprehension for objects specified with attributes. The proposed Grounding DINO model performs remarkably well on all three settings and achieves impressive results on benchmark datasets such as COCO (Common Objects in Context), LVIS (Large Vocabulary Instance Segmentation), ODinW (Open Domain Object Detection in the Wild) and RefCOCO/+/g (ReferItGame). Notably, Grounding DINO achieves an impressive average precision (AP) of $52.5$ on the COCO detection zero shot transfer benchmark without any training data from COCO. It also sets a new record on the ODinW zero shot benchmark with a mean AP of $26.1$. The code for implementing Grounding DINO will be made available at \url{https://github.com/IDEA Research/GroundingDINO}. Overall, the proposed Grounding DINO model demonstrates significant advancements in open set object detection by effectively incorporating language and vision modalities. The impressive performance on various benchmark datasets showcases its potential for real world applications in object detection tasks.
- Authors present a novel open-set object detector called Grounding DINO
- Grounding DINO combines DINO with grounded pre-training for object detection using human inputs
- Proposed approach includes feature enhancer, language-guided query selection mechanism, and cross-modality decoder for fusion
- Evaluation includes referring expression comprehension for objects specified with attributes
- Grounding DINO performs well on COCO, LVIS, ODinW, and RefCOCO/+/g datasets
- Achieves impressive results on zero-shot transfer benchmarks without training data from COCO
- Code for implementing Grounding DINO will be available at the provided URL
- Demonstrates significant advancements in open set object detection by incorporating language and vision modalities
Authors have created a new object detector called Grounding DINO. It combines two things called DINO and grounded pre-training to find objects. They used human inputs to help train it. The approach they used has different parts like a feature enhancer, a way to choose which questions to ask, and a way to combine different types of information. They tested how well it worked by asking the detector questions about objects with certain characteristics. It did really well on different datasets and even without using certain training data. The code for this detector will be available online. This new detector is a big improvement in finding objects because it uses both language and vision."
Definitions
- Object Detector: A tool or program that can find and identify objects in pictures or videos.
- Open-set: A type of problem where the object detector needs to be able to recognize objects that it hasn't seen before.
- Grounded Pre-training: A method of training an object detector using human inputs or information.
- Feature Enhancer: Something that helps make the features or characteristics of an object stand out more so the detector can find it better.
- Language-guided Query Selection Mechanism: A way of choosing which questions or queries to ask the object detector based on language or words.
- Cross-modality Decoder for Fusion: A method of combining different types of information, like language and vision, together so they work well together.
- Referring Expression Comprehension: Understanding what someone means when they describe an object using words or
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
Open set object detection is a challenging task in computer vision, where the goal is to detect objects from an open set of categories. Traditional closed-set detectors are limited in their ability to generalize concepts and handle open-set scenarios. To address this challenge, researchers have proposed various approaches such as combining language and vision modalities or incorporating pre-training techniques. In this paper titled "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection," authors Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu and Lei Zhang present a novel open-set object detector called Grounding DINO that combines the Transformer-based detector DINO with grounded pre-training to detect arbitrary objects using human inputs such as category names or referring expressions.
Overview of the Proposed Approach
The key challenge in open set object detection is generalizing concepts from closed set detectors to handle open set scenarios. To address this challenge and effectively fuse language and vision modalities, the authors propose a three phase approach for conceptually dividing a closed set detector. The first phase involves enhancing feature representations by leveraging semantic information from external sources such as natural language descriptions or referring expressions. The second phase focuses on selecting queries based on language guidance while also taking into account visual cues from images. Finally the third phase involves cross modality fusion between language features and visual features via a decoder module which outputs predictions for each query instance along with confidence scores.
Evaluation Results
While previous works mainly focus on evaluating open set object detection on novel categories ,the authors propose an additional evaluation on referring expression comprehension for objects specified with attributes . The proposed Grounding DINO model performs remarkably well on all three settings and achieves impressive results on benchmark datasets such as COCO (Common Objects in Context), LVIS (Large Vocabulary Instance Segmentation), ODinW (Open Domain Object Detection in the Wild) and RefCOCO/+/g (ReferItGame). Notably ,Grounding Dino achieves an impressive average precision (AP) of $52.5$ on the COCO detection zero shot transfer benchmark without any training data from COCO . It also sets a new record on the ODinW zero shot benchmark with a mean AP of $26$.1 . The code for implementing Grounding Dino will be made available at \url{https://github.com/IDEA Research/GroundingDino}.
Conclusion
Overall ,the proposed Grounding Dino model demonstrates significant advancements in open set object detection by effectively incorporating language and vision modalities . The impressive performance on various benchmark datasets showcases its potential for real world applications in object detection tasks
