Vision-Language Scene Recognition with Fine-Grained Object Augmentation
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Solution Overview
Problem
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 results for the depicted objects.
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 outputs, replacing coarse-grained terms with fine-grained object recognition details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a vision language model is used to generate language output for image description, then the output is scene-aware and descriptive, but the object identification is coarse-grained and lacks fine-grained details
Solution Approach 1:
The patent combines the vision language model and object recognition model into a unified processing system that generates both scene descriptions and fine-grained object identifiers simultaneously. The language output from the vision model is merged with the fine-grained output from the object recognition model, replacing coarse object terms with precise identifiers while maintaining scene context.
Solution Approach 2:
The processing system is segmented into two specialized components: a vision language model for scene understanding and natural language generation, and an object recognition model for fine-grained object identification. This segmentation allows each component to excel at its specific task while their outputs are integrated to provide comprehensive image description with precise object identification.
2Measurement precision
If text searching is used alone to understand image content, then the process is simple, but the search queries are not descriptive enough and yield undesired results
Solution Approach 1:
The system performs preliminary image analysis using both vision language modeling and object recognition before generating search queries. This preliminary action extracts fine-grained object identifiers and scene context in advance, which are then used to construct highly relevant search queries that yield accurate results on first attempt, reducing the need for iterative searching.
3Adaptability or versatility
If a single model is used for both scene description and object recognition, then the system is simpler, but it cannot generate both coarse scene awareness and fine-grained object details simultaneously
Solution Approach 1:
The integrated system achieves multi-functionality by combining two specialized models that work together to provide both scene-level and object-level analysis. The vision language model provides universal scene understanding and context, while the object recognition model adds specialized fine-grained identification capabilities, creating a versatile system that handles multiple analysis granularities simultaneously.
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.