I. Multiple Challenges in Referring Expression Comprehension
At the intersection of computer vision and natural language processing, Referring Expression Comprehension (REC) has emerged as a critical research direction. This task requires models to precisely locate objects in images based on natural language descriptions.
However, traditional vision models show obvious limitations when facing multi-instance referring tasks. Most of them are optimized only for single-instance scenarios and struggle to deal with the complex real-world situations where "one instruction corresponds to multiple objects". This shortcoming stems from their superficial processing of language—they lack the capacity to effectively parse grammatical structures and comprehend the semantic logic inherent in natural language expressions.
II. A New Paradigm of Reasoning: From Noun Recognition to Understanding Referring Logic
By integrating DINO-X, a unified vision model, with a multimodal large-language model, DINO-XSeek combines precise detection capabilities with powerful reasoning and understanding abilities. This integration transcends the superficial language understanding limitations of traditional vision models. Specifically, DINO-XSeek has developed advanced cognitive capabilities across three distinct levels: vocabulary, grammar, and referring logic.
2.1 Vocabulary Level: From Noun to Multi-Part-of-Speech Understanding
Traditional models mainly focus on noun recognition, while DINO-XSeek can understand a broader spectrum of linguistic elements:
(a) Adjective: Modifiers describing object attributes, such as "black" or "round";
(b) Verb: Words depicting behavioral states, like "running" or "holding";
(c) Preposition: Terms expressing spatial relationships, including "above," "between," etc.
For instance, when processing the description "unripe tomato," DINO-XSeek understands both "unripe" (adjective) and "tomato" (noun), recognizing the modifying relationship between them. The system first identifies all tomatoes in the scene, then applies attribute analysis to distinguish the "unripe tomato" from all other tomatoes.
Figure 1: DINO-XSeek annotates "unripe tomato"
2.2 Grammatical Level: Understanding Syntactic Structure and Dependency Relationships
DINO-XSeek can analyze the grammatical structure of sentences and understand the dependency relationships between words, including:
(a) Subject-predicate relationship: Distinguishing the subject and action in phrases like "a person is walking";
(b) Modifying relationship: Differentiating between color and size modifications in expressions such as "a big red car";
(c) Possessive relationship: Comprehending subordinate relationships in phrases like "the back seat of the car".
For example, when processing "The worker under the steel bars," DINO-XSeek accurately analyzes the grammatical dependencies among "worker" (noun), "steel bars" (noun), and "under" (preposition), understanding how the adverbial phrase "under the steel bars" modifies the subject "worker."
The system first identifies all workers in the scene, then employs spatial analysis to determine which workers are positioned "under the steel bars," precisely identifying specific workers in potentially hazardous positions.
Figure 2: DINO-XSeek annotates "the worker under the steel bars"
2.3 Semantic and Logical Level: Multi-Step Reasoning
Building on its vocabulary and grammatical comprehension capabilities, DINO-XSeek demonstrates sophisticated high-level semantic reasoning, handling complex instructions that require multi-step logical analysis.
For instance, when processing the instruction "People who are below the rock climbing wall but are not sitting," the model executes a complex logical analysis:
(a) Identifies all instances of "people"
(b) Filter out the people who are "below the rock climbing wall";
(c) Identify all "non-sitting" targets in this area.
This multi-step logical reasoning ability enables DINO-XSeek to handle complex language instructions in the real world. At the same time, it also means that DINO-XSeek has the ability to directly perform object detection tasks according to the business logic described by users, shifting from a traditional "object-centered" approach to one focused on "understanding object attributes and relationships." This eliminates the need for cumbersome secondary processing typical of traditional vision-based models and significantly reduces post-development costs in production environments.
Figure 3: DINO-XSeek annotates "people who are below the rock climbing wall but are not sitting"
III. Technical Architecture: The Coordination Mechanism of Detection and Understanding
DINO-XSeek employs a hybrid architecture to address the dual limitations of traditional models: object detection models' lack of language understanding and language models' inability to precisely position objects. Its retrieval-based framework operates in two key stages:
(a) Visual Perception Stage: Utilizes DINO-X, an open-world object detection model, to scan images and generate bounding boxes and feature representations for all potential objects;
(b) Language Understanding Stage: Leverages a large language model to parse natural language descriptions, comprehend attribute requirements, spatial relationships, interaction behaviors, and logical conditions, then retrieves the set of objects meeting these criteria from detected candidates.
The core processing flow incorporates three components: a vision encoder extracting vision tokens from images, an object detection model extracting object tokens, and a tokenizer processing text inputs. These three token types deeply interact through the large language model to achieve comprehensive reasoning.
Figure 4: Overview of the DINO-XSeek model
Through architectural innovation, DINO-XSeek demonstrates four core advantages over traditional models:
(a) Multimodal Integration: Achieves seamless integration of visual and linguistic information, enabling the system to both "see" objects in images and "understand" the complex relationships between these objects and language descriptions.
(b) Enhanced Robustness: Significantly improves processing capabilities for irregularly shaped objects, partially occluded scenes, and dense multi-instance environments, maintaining stable performance in complex real-world settings.
(c) High-Level Semantic Reasoning: Leverages the powerful reasoning capabilities of large language models to handle complex instructions containing multiple conditions and implicit relationships.
(d) Configuration Flexibility: Allows users to flexibly configure detection strategies through natural language descriptions without writing complex code or adjusting model parameters, thus greatly reducing the technical threshold and development costs.
These advantages position DINO-XSeek as a crucial bridge connecting advanced visual understanding with natural language interaction, opening new possibilities for industrial applications.
Conclusion: The New Era of Vision Models — From Perception to Cognition
By integrating the technical strengths of object detection and large language models, DINO-XSeek achieves a significant leap from simple object recognition to complex referential understanding, marking computer vision's evolution from basic "perception" to advanced "cognition." This technological breakthrough not only addresses fundamental challenges in multi-instance referring tasks but also establishes a new paradigm for human-machine interaction.
As these technologies continue to develop and find broader applications, artificial intelligence will play increasingly sophisticated roles across industries. In manufacturing, it can precisely identify and classify various defects; in smart cities, it can monitor abnormal behaviors and alert to potential risks; in agriculture, it can distinguish crop growth states and optimize resource allocation; in autonomous driving, it can comprehend complex road environments and make safe decisions.
DINO-XSeek represents not merely a technological innovation but a critical step toward true artificial intelligence. By dissolving barriers between traditional vision models and natural language processing, it creates more intuitive and efficient pathways for future human-machine collaboration. This allows humans to communicate with AI systems through natural language, integrating human cognitive abilities with machine computational power to collectively address complex real-world challenges.
Appendix
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DINO-XSeek blog：https://deepdataspace.com/blog/dino-xseek
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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-XSeek model online, please visit DINO-X Playground: https://cloud.deepdataspace.com/playground/dino-x?referring_prompt=0