Joint Object and Relationship Detection With Density-Aware Loss
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Solution Overview
Problem
Existing automated image analysis systems separately optimize object detection and relationship prediction, preventing effective cross-learning and co-adaptation between the two domains, leading to suboptimal performance in detecting objects and their relationships.
Innovation Solution
A joint training approach for object detection and relationship prediction models using a keypoint-based model with a shared backbone layer, where object detection is performed using object heatmaps and relationship prediction is based on relation feature maps, with a combined loss function that weights higher-density and lower-confidence predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate optimization is used for object detection and relationship prediction, then each model can be independently trained, but the two models cannot co-adapt each other to maximize performance and information from the relationship model does not flow to detection
Solution Approach 1:
The patent combines object detection and relationship prediction into a single unified model architecture where both tasks share common backbone layers and are trained simultaneously through a joint loss function. This merging enables gradient flow between tasks, allowing the models to co-adapt and share learned features, thereby resolving the contradiction between independent training ease and joint performance optimization.
2Device complexity
If two-stage approach is used with separate models, then model structure is simpler, but the detection model has no knowledge of the relationship task and the relationship model uses detection predictions as fixed inputs
Solution Approach 1:
The unified model architecture implements multi-functionality by designing a shared backbone that serves both object detection and relationship prediction tasks. The same feature extraction layers are utilized by both task-specific heads, enabling the model to perform multiple functions with a single structure. This resolves the contradiction by making the model adaptable to both tasks simultaneously while maintaining structural efficiency.
3Ease of manufacture
If separate models are used, then training can be performed independently, but relationship labels provide additional information that is not available during the object detection phase
Solution Approach 1:
The joint training framework enables continuous utilization of all available information throughout the training process. By formulating a combined loss function that incorporates both detection loss and relationship prediction loss, the system continuously leverages relationship labels during training to guide feature learning. This continuous information utilization resolves the contradiction by preventing information loss while maintaining training efficiency through unified optimization.
Data Source
AI summary
An object detection model and relationship prediction model are jointly trained with parameters that may be updated through a joint backbone. The offset detection model predicts object locations based on keypoint detection, such as a heatmap local peak, enabling disambiguation of objects. The relationship prediction model may predict a relationship between detected objects and be trained with a joint loss with the object detection model. The loss may include terms for object connectedness and model confidence, enabling training to focus first on highly-connected objects and later on lower-confidence items.