Natural Language Object Localization With Unified Image-Text Fusion
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Solution Overview
Problem
Existing object localization models struggle to effectively integrate visual grounding and detection tasks, leading to limited performance in handling complex queries, multiple objects, and generalization to novel categories, with separate models failing to leverage the strengths of each task.
Innovation Solution
A unified object localization system (OLS) framework that employs a multi-level image-text feature fusion using cross-attention to integrate referring expression comprehension, object localization, and object detection, enabling flexible and efficient localization of objects across various scales and queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for visual grounding and object detection tasks, then each model can be optimized for its specific task, but the overall system fails to leverage the strengths of each task and achieves limited performance on complex queries
Solution Approach 1:
The patent combines visual grounding and object detection tasks into a unified model framework. The model simultaneously processes referring expressions and performs detection, allowing it to leverage strengths from both tasks. The unified architecture shares feature extraction components and uses joint training to improve overall performance on complex queries and multiple object localization.
Solution Approach 2:
The unified model is designed to perform multiple functions: it can handle referring expression comprehension, object localization, and object detection tasks within a single framework. The model accepts diverse input types (referring expressions, detection queries) and produces appropriate outputs (bounding boxes, segmentation masks) based on the task requirements, demonstrating multi-functionality.
2Adaptability or versatility
If a unified model is created to handle multiple tasks, then the system can leverage strengths of each task and improve generalization, but the model complexity increases
Solution Approach 1:
The unified model is segmented into distinct functional components: a shared feature extraction backbone, task-specific projection layers, and separate output heads for different tasks. This segmentation allows the model to handle multiple tasks while maintaining modularity and managing complexity through organized functional separation.
Solution Approach 2:
The model uses parameter sharing across tasks to reduce complexity. The shared backbone parameters are trained jointly on multiple tasks, allowing the system to leverage common features while adapting to different task requirements through task-specific projection layers with separate parameters. This parameter sharing strategy reduces the total number of parameters compared to fully separate models.
Data Source
AI summary
Generally, the disclosure is directed to generalized objected location, where the located object is in accordance to a natural language (NL) query. More specifically, the embodiments include a unified generalized visual localization architecture. The architecture achieves enhanced performance on the following three tasks: referring expression comprehension, object localization, and object detection. The embodiments employ machine-learned NL models and/or image models. The architecture is enabled to understand and answer natural localization questions towards an image, to output multiple boxes, provide no output if the object is not present (e.g., a null result), as well as, solve general detection tasks.