Generative Language Model Semantic Labeling Novel Concepts
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Solution Overview
Problem
Existing machine perception methods struggle with accurately localizing and recognizing objects in real-world scenarios, particularly when novel concepts arise, due to reliance on predefined class names and manual intervention for label definition conflicts across datasets.
Innovation Solution
The OmniScient Model (OSM) is introduced, which uses a generative framework to predict class labels in a text generation manner, eliminating the need for predefined class names and enabling cross-dataset training without human intervention, by leveraging a pre-trained Large Language Model (LLM) for world knowledge and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined class names and manual label definition are used, then label consistency across datasets can be maintained, but adaptability to novel concepts and real-world scenarios deteriorates
Solution Approach 1:
The patent introduces a visual resampler as an intermediary component between the image encoder and language model. This resampler transforms image features into a format that bridges the gap between visual data and linguistic knowledge, enabling the system to handle novel concepts without requiring predefined labels while maintaining reliability through the structured mediation of the resampler module.
Solution Approach 2:
The system employs a generative language model that leverages its inherent world knowledge to automatically generate semantic labels for novel concepts without requiring manual intervention or predefined class names. The model serves itself by utilizing its pre-trained linguistic understanding to adapt to new concepts, eliminating the need for external label definition while maintaining consistency through its internal knowledge representation.
2Adaptability or versatility
If generative language model with pre-trained LLM is used, then adaptability to novel concepts improves, but device complexity increases
Solution Approach 1:
The patent segments the complex generative model architecture into distinct functional modules: an image encoder for feature extraction, a visual resampler for feature transformation, and a language model for label generation. This segmentation allows each component to be optimized independently and facilitates easier deployment and management of the overall system, reducing the practical complexity despite the advanced capabilities.
Solution Approach 2:
The visual resampler serves multiple functions within the architecture: it transforms image features into a suitable representation for the language model, enables cross-dataset training without manual intervention, and facilitates the integration of pre-trained LLM knowledge. This multi-functionality reduces the need for separate specialized components, thereby managing complexity while maintaining high adaptability.
3Productivity
If manual intervention for label definition conflicts is eliminated, then productivity improves, but measurement precision of object recognition may worsen
Solution Approach 1:
The system implements a feedback mechanism where the generative language model continuously refines its predictions by leveraging its pre-trained world knowledge. The model receives image features, generates semantic labels, and uses its internal knowledge representation to correct and improve predictions, ensuring high precision without requiring manual intervention for label definition conflicts across different datasets.
Solution Approach 2:
The patent applies preliminary action by pre-training the language model on extensive world knowledge before deployment. This pre-training equips the model with robust semantic understanding and label definition capabilities, allowing it to automatically handle cross-dataset training scenarios with high precision without requiring manual intervention during the actual training process, thus maintaining both productivity and measurement precision.
Data Source
AI summary
A computing system including one or more processing devices configured to receive an image. The processing devices are further configured to compute a segmentation mask that identifies a region of interest included in the image. At a feature extractor, the processing devices are further configured to compute encoded image features based on the image. The processing devices are further configured to receive a text instruction. At a visual resampler, the processing devices are further configured to compute a mask query based on the segmentation mask, the encoded image features, and the text instruction. At a generative language model, the processing devices are further configured to receive a natural language query that includes the mask query and the text instruction. Based on the natural language query, at the generative language model, the processing devices are further configured to generate and output a semantic label associated with the region of interest.