Natural Language Object Selection via Dynamic Model Switching
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Solution Overview
Problem
Conventional image editing systems are inflexible, inaccurate, and inefficient in detecting and selecting objects in digital images, particularly when using natural language inputs, as they struggle with recognizing slang terms, granular terms, and complex relationships between objects, leading to incorrect and resource-intensive processing.
Innovation Solution
The implementation of an object selection system that utilizes natural language processing tools to identify object terms and relationships, generates an object selection pipeline with interchangeable neural networks, and employs mapping tables to find alternative terms for unrecognized objects, enabling accurate and efficient detection and selection of query objects based on natural language queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use standard object detection methods, then processing is straightforward, but they fail to recognize slang terms and granular terms accurately
Solution Approach 1:
The system dynamically adjusts its object recognition approach by switching between different detection models based on the query type. It uses specialized granular object detection models for specific terms, slang term detection models for informal language, and relationship-based detection for contextual queries, rather than relying on a single static detection method
Solution Approach 2:
The system changes the parameters of object detection by using different detection models with varying levels of granularity and specialization. It adjusts the detection sensitivity and model selection based on the specific query requirements, allowing accurate recognition across different language styles and object types
2Extent of automation
If conventional systems use end-to-end neural networks, then automation is high, but fault localization and system adjustment are difficult
Solution Approach 1:
The system segments the object detection process into distinct modular components: query analysis module, object detection module (with specialized sub-models), relationship detection module, and result generation module. Each module can be independently tested, adjusted, and repaired without retraining the entire system, enabling easy fault localization and system maintenance
Solution Approach 2:
The system introduces intermediate processing layers between the input query and final output, including query analysis intermediaries that break down the query into components, and detection intermediaries that apply different specialized models. These intermediaries make the system more transparent and easier to debug while maintaining automation
3Measurement precision
If conventional systems manually select objects, then precision can be achieved, but user interaction time and computing resources are wasted
Solution Approach 1:
The system performs automatic object selection by analyzing the natural language query, detecting relevant objects using specialized models, and determining relationships between objects without requiring manual user selection. The system serves itself by making intelligent selections based on query context, eliminating the need for time-consuming manual interaction while maintaining high precision
4Adaptability or versatility
If conventional systems use generic relationship operators, then system complexity is low, but they cannot handle complex object relationships
Solution Approach 1:
The system implements a universal relationship detection framework that can handle multiple types of relationships (spatial, temporal, causal, hierarchical) through a single integrated approach. The relationship detection module universally applies to all object pairs detected in the image, adapting to different relationship types without requiring separate specialized systems for each relationship category
Data Source
AI summary
The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image based on natural language-based inputs. For instance, the object selection system can utilize natural language processing tools to detect objects and their corresponding relationships within natural language object selection queries. For example, the object selection system can determine alternative object terms for unrecognized objects in a natural language object selection query. As another example, the object selection system can determine multiple types of relationships between objects in a natural language object selection query and utilize different object relationship models to select the requested query object.