Vision-Language Scene Recognition With Fine-Grained Object Fusion
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Solution Overview
Problem
Existing vision language models struggle with generating detailed and specific outputs for instance-level object recognition, leading to generalized queries and prompts that may not provide relevant information for the depicted objects in images.
Innovation Solution
Parallel processing of input images using both a vision language model and a fine-grained object recognition model to generate an augmented language output that combines scene-aware and object-aware descriptions, replacing coarse-grained terms with fine-grained object recognition outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a vision language model is used to generate language output, then scene-aware description is improved, but object-level recognition precision deteriorates
Solution Approach 1:
The system segments the image processing task into two parallel streams: one for scene-level understanding (vision language model) and one for object-level recognition (fine-grained object recognition model). This segmentation allows each model to specialize in its respective task, with the scene-aware model providing contextual understanding and the object recognition model providing precise object identification, thereby resolving the contradiction between scene description quality and object recognition precision.
2Adaptability or versatility
If coarse-grained terms are used in language output, then generality is improved, but specificity of object identification deteriorates
Solution Approach 1:
The system merges the output of two models: the vision language model generates coarse-grained scene descriptions with high adaptability, while the fine-grained object recognition model generates specific object identifiers. These outputs are combined in the augmented language output, allowing the system to maintain both generality and specificity simultaneously by integrating different levels of description granularity.
3Device complexity
If only vision language model processing is used, then system complexity is reduced, but output detail and accuracy deteriorate
Solution Approach 1:
The processing system is segmented into two specialized models working in parallel: a vision language model for scene understanding and a fine-grained object recognition model for precise object identification. This segmentation increases output detail and accuracy by leveraging the strengths of each model, while the modular architecture manages system complexity through clear separation of concerns and independent model optimization.
Data Source
AI summary
Systems and methods for image understanding can include one or more object recognition systems and one or more vision language models to generate an augmented language output that can be both scene-aware and object-aware. The systems and methods can process an input image with an object recognition model to generate an object recognition output descriptive of identification details for an object depicted in the input image. The systems and methods can include processing the input image with a vision language model to generate a language output descriptive of a predicted scene description. The object recognition output can then be utilized to augment the language output to generate an augmented language output that includes the scene understanding of the language output with the specificity of the object recognition output.