I. Introduction
Grounding DINO has driven significant advancements in open-set object detection by combining the Transformer-based DINO detection architecture with the advantages of Grounded Pre-Training technology. This fusion enables the model to detect arbitrarily specified objects through natural language input, breaking away from the previous paradigm of object detection based on predefined categories.
To address the limitations of existing open-world detection systems, such as handling long-tail distributions (where some objects appear frequently while many others are rare) and supporting multiple input modalities, the IDEA-CVR team built DINO-X on the foundation of Grounding DINO. What sets DINO-X apart is its ability to accept not just text prompts but also visual prompts and custom prompts, making it adaptable to diverse detection scenarios.
Additionally, DINO-X integrates specialized modules for segmentation, keypoint detection, and language understanding, thereby creating a comprehensive image analysis framework.
Figure 1 DINO-X is a unified object-centric vision model which supports various open-world perception and object-level understanding tasks, including Open-World Object Detection and Segmentation, Phrase Grounding, Visual Prompt Counting, Pose Estimation, Prompt-Free Object Detection and Recognition, Dense Region Caption, etc.
1. Model Architecture
DINO-X is built on a Transformer encoder-decoder architecture, with a design similar to Grounding DINO 1.5. The architecture includes several key components:
(1) Visual Backbone: This is a pre-trained Vision Transformer (ViT) that processes input images and extracts visual features. This network functions like the human eye and visual cortex, responsible for "seeing" images and extracting key features. For example, when seeing a cat, it would notice features like ears, tail, and limbs.
(2) Text Encoder: A CLIP text encoder processes text prompts, enabling language-guided detection. This encoder acts as a language understanding center, converting text prompts like "find the red fire hydrant" into representations the model can understand.
(3) Prompt Handling: The model supports three types of prompts:
(a) Text prompts for language-guided detection;
(b) Visual prompts for example-based detection, such as showing it an image of an apple to find all apples in the target image;
(c) Custom prompts combining text and visual information, such as providing a text description of "red" and an image of an apple to find all red apples.
(4) Transformer Encoder-Decoder: Processes visual features and prompt information to locate objects in the image.
(5) Task-Specific Heads: Multiple specialized heads responsible for implementing different perception tasks:
(a) Detection head for object localization (indicating "where that object is");
(b) Mask head for instance segmentation (precisely delineating "where exactly the object's boundaries are");
(c) Keypoint head for pose estimation (marking "where the person's elbows and knees are");
(d) Language head for object-level language understanding (describing "this is a small brown dog running on the grass").
For example, when you give DINO-X a photo of a park and prompt "find all dogs," the system analyzes the image through the visual backbone, understands the concept of "dog" through the text encoder, then integrates this information through the encoder-decoder. Finally, the detection head frames all dog locations, the mask head precisely depicts each dog's outline, the keypoint head might mark the joint positions of the dogs, and the language head might generate descriptions like "a brown Labrador running on the grass."
This unified architecture allows the model to perform efficient multi-task learning and inference, thereby reducing the need and inconvenience for users to use separate models for different perception tasks.
2. Training Method
DINO-X employs a sophisticated training method that enables it to learn effectively across multiple tasks:
2.1 Dataset Creation
Researchers created a dataset called "Grounding-100M," containing over 100 million high-quality annotated samples. Simultaneously, they used open-source segmentation models to generate pseudo-mask annotations for a subset of this dataset, used for training the mask module.
2.2 Two-Stage Training
The training process follows a two-stage approach:
(1) First Stage: Joint training based on three methods, including: a) text prompt-based detection; b) visual prompt-based detection; c) object segmentation. For example, the model first learns how to identify a fire truck through the text "red fire truck" or an image of a fire truck, while also learning to precisely outline the fire truck.
(2) Second Stage: Additional perception heads (keypoints, language) are added and trained while keeping the backbone frozen. The keypoint head is trained only on human pose datasets, and the language head only on region description datasets. This phased learning effectively prevents new knowledge from diluting old knowledge, just as a person who has mastered the basics of painting can further learn sketching or oil painting without forgetting basic techniques.
3. Key Capabilities
Building upon the capabilities of Grounding DINO, DINO-X offers even more powerful key capabilities, including:
Figure 2 Comparison of Grounding DINO 1.5 Pro, Grounding DINO 1.6 Pro and DINO-X
3.1 Multimodal Prompting
DINO-X's prompting system is highly flexible, similar to multiple ways of communicating with a smart assistant:
(1) Text Prompts: Users can describe objects in natural language (e.g., "find all cats in the image").
(2) Visual Prompts: Users can provide example images of objects they want to detect, enabling few-shot detection (e.g., showing the model an image of a Tesla car allows it to identify all Tesla models in a street scene).
(3) Custom Prompts: Combining text and visual information for more precise detection (e.g., "find all objects of the same type that are larger than this example" while providing an image of a small sailboat).
This flexibility allows DINO-X to adapt to a wider range of detection scenarios and user needs.
3.2 Long-Tailed Recognition
DINO-X shows significant improvement in detecting rare object categories on the LVIS benchmark, overcoming important limitations of previous models. This capability makes DINO-X particularly valuable in specialized fields (such as biodiversity monitoring, rare artifact identification). The model achieves this primarily through:
(1) Extensive training on the diverse Grounding-100M dataset;
(2) Effective utilization of the pre-trained CLIP encoder, which has strong zero-shot capabilities;
(3) Support for multimodal prompts, allowing visual examples for rare categories.
3.3 Comprehensive Object Understanding
Beyond simple detection, DINO-X can provide rich information including:
(1) Instance Segmentation: Precisely marks object boundaries, such as accurately delineating tumor areas in medical images;
(2) Keypoint Detection: Marks important points, such as labeling athletes' joint positions in sports analysis, helping analyze shooting postures or running stances. DINO-X achieved competitive results on human 2D keypoint detection benchmarks and excellent performance in hand pose estimation on the HInt benchmark.
(3) Language Understanding: Object recognition, region description, text recognition, and region-based visual question answering, such as describing a part of a street scene as "a busy café with several customers talking in the outdoor seating area." DINO-X's language head demonstrated effective performance on object recognition benchmarks, achieving high semantic similarity and semantic IoU (Intersection over Union) scores. This comprehensive approach enables deep understanding of detected objects.
4. DINO-X Edge
The DINO-X research team also developed DINO-X Edge through knowledge distillation technology, an optimized version for deployment on resource-constrained devices, much like an experienced teacher (the Pro model) condensing and passing on years of experience to a student (the Edge model). For example, the full model might know 1,000 subtle features for identifying cats, while through distillation, the lightweight model might retain only the 100 most important features, yet still achieve similar recognition accuracy.
II. Application Scenarios
DINO-X's capabilities make it suitable for numerous practical scenarios:
1. Autonomous Driving and Robot Navigation
Autonomous driving systems can use DINO-X to identify various objects on the road, including those uncommon in training data (such as road debris, unusual vehicle types, or animals). For example, even if the system never specifically learned about "electric self-balancing scooters," when a person riding one appears on the road, DINO-X can still identify it as a "person riding a mobile device" and take appropriate avoidance actions.
2. Retail and Inventory Management
Stores can use DINO-X to identify products on shelves and automatically detect stockouts. For example, a supermarket manager can simply prompt the system to "find all out-of-stock shelf positions," and the system will automatically generate a restocking list. For new products, simply showing a product image allows the system to track the placement and quantity of such products throughout the store.
3. Medical Image Analysis
Doctors can use DINO-X to assist in analyzing X-rays, CT scans, and MRI images. Through simple text prompts like "find lung abnormalities" or visual prompts (providing example images from similar cases), the system can help mark potential problem areas, speeding up the diagnostic process. In dermatology, doctors can use the visual prompt feature to upload images of specific skin lesions, having the system find all similar lesions in full-body skin scans.
4. Smart Manufacturing and Quality Inspection
Factories can use DINO-X for automated quality inspection. For example, showing the system a few images of defective products as visual prompts allows it to automatically identify similar defects on the production line. Even for new types of defects, quality inspectors only need to provide a few examples, and the system can quickly adapt and begin detection.
5. Environmental Monitoring and Wildlife Conservation
Ecologists can use DINO-X to analyze images captured by camera traps in the wild, identifying and counting various animal species, including rare ones. For example, by providing a few images of snow leopards as visual prompts, the system can find all possible snow leopard appearances from thousands of camera trap images, greatly improving research efficiency.
6. Edge Computing
The Edge version makes deployment on resource-constrained devices (such as mobile phones and embedded systems) possible, making advanced computer vision available in more scenarios.
Conclusion
DINO-X represents a significant advancement in open-world object detection and understanding. By unifying multiple perception tasks into a single model and supporting flexible prompt mechanisms, it provides a versatile framework for comprehensive image analysis.
The model's ability to handle long-tail distributions and its strong performance on rare object categories address important limitations of previous methods. Additionally, the development of the optimized Edge version makes this technology more widespread in practical applications on resource-constrained devices, bringing advanced computer vision capabilities beyond high-performance servers and into our everyday devices.
As computer vision continues to evolve, models like DINO-X that can adapt to user needs through flexible prompts and handle the diversity of real-world objects will play an increasingly important role in applications ranging from medical diagnostics to smart manufacturing, from autonomous driving to personal assistants, ultimately helping us create smarter technology products that better understand human needs.
Appendix
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Paper: "DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding," Authors: Tianhe Ren, Yihao Chen, Qing Jiang, Zhaoyang Zeng, Yuda Xiong, Wenlong Liu, Zhengyu Ma, Junyi Shen, Yuan Gao, Xiaoke Jiang, Xingyu Chen, Zhuheng Song, Yuhong Zhang, Hongjie Huang, Han Gao, Shilong Liu, Hao Zhang, Feng Li, Kent Yu, Lei Zhang. Link: https://arxiv.org/abs/2411.14347
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To use the latest DINO API, please visit the DINO-X Platform: https://cloud.deepdataspace.com
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To experience the latest DINO-X model online, please visit DINO-X Playground: https://cloud.deepdataspace.com/playground/dino-x