Image-Conditioned Prefix Language Modeling for Attribute Recognition
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Solution Overview
Problem
Existing computer vision systems struggle with accurate attribute recognition for objects in images, often failing to identify relevant attributes and associating them with incorrect objects.
Innovation Solution
A computer-implemented method that leverages generative prompting by processing image and text data with a language model and a pre-trained image-text model to determine probability scores for candidate attributes, ensuring accurate association with the object of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing computer vision systems use traditional classification methods, then processing speed is maintained at acceptable levels, but attribute recognition accuracy deteriorates significantly
Solution Approach 1:
The patent introduces a language model as an intermediary component that bridges image processing and attribute recognition. The language model processes text data and generates candidate attributes, which are then evaluated by the image-text model. This intermediary approach allows the system to leverage linguistic knowledge to improve attribute recognition accuracy without directly increasing the complexity of the vision processing pipeline.
Solution Approach 2:
The system segments the attribute recognition task into multiple independent components: (1) text data processing by a language model to generate candidate attributes, (2) image processing by an image-text model to evaluate candidates, and (3) probability score calculation to determine the final attribute. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while improving accuracy.
2Measurement precision
If the system processes image data alone to identify attributes, then processing time is reduced, but attribute association accuracy deteriorates due to inability to distinguish object-specific attributes from background attributes
Solution Approach 1:
The patent merges text data processing and image data processing into a unified attribute recognition framework. The language model processes text data to generate candidate attributes, while the image-text model simultaneously processes image data to evaluate these candidates. This merging of textual and visual processing streams enables the system to accurately associate attributes with specific objects by considering both text context and visual evidence, preventing misassociation with background elements.
3Measurement precision
If the system uses extensive training of image-text models to improve attribute recognition, then recognition accuracy improves, but computational efficiency deteriorates
Solution Approach 1:
The language model performs preliminary action by pre-processing text data and generating candidate attributes before the image-text model processes the images. This preliminary text processing step reduces the computational burden on the image-text model, as it only needs to evaluate a finite set of pre-generated candidates rather than performing comprehensive attribute recognition from scratch. This division of labor improves overall computational efficiency while maintaining high accuracy.
Data Source
AI summary
Systems and methods for attribute recognition can include obtaining an image and a text string. The text string can be processed with a language model to generate a set of candidate attributes based on sequence based prediction. The image and the candidate attributes can be processed with an image-text model to determine a likelihood that the respective candidate attribute is depicted in the image. The likelihood determination can then be utilized to determine a predicted attribute for the object of interest.