Scene Graph Enrichment Using Sequential Node-Edge Prediction
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Solution Overview
Problem
Conventional scene creation tools require significant time and computing resources due to generalized and irrelevant recommendations of objects and relationships, lacking the ability to reliably suggest semantically related nodes and underrepresented edges based on a unique initial seed scene.
Innovation Solution
A scene enrichment system that modifies machine learning models' loss functions to incorporate external knowledge, class balancing, and historic relationships, transforming scene graph expansion into a sequential prediction task to provide diverse and coherent recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional scene creation tools use generalized recommendation templates, then device complexity is reduced, but manufacturing precision deteriorates because recommendations are not contextually relevant
Solution Approach 1:
The system changes parameters of the recommendation engine by incorporating multiple loss function components (coherence loss, diversity loss, external knowledge loss) that dynamically adjust recommendation quality based on scene context, seed diversity, and semantic relationships rather than using fixed template-based parameters
Solution Approach 2:
The system performs preliminary scene graph extraction and analysis of the seed scene before generating recommendations, preparing contextual information and semantic relationships in advance to guide subsequent recommendation generation, thereby improving relevance without increasing operational complexity
2Manufacturing precision
If the recommendation system runs multiple times to find relevant recommendations, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The system implements feedback mechanisms where the loss function continuously evaluates recommendation quality against coherence, diversity, and external knowledge criteria, allowing the model to learn from previous predictions and improve recommendation quality in a single pass without requiring multiple reruns
Solution Approach 2:
The scene graph expansion operates as a continuous sequential prediction process where each predicted node and edge builds upon previous predictions, maintaining contextual coherence throughout the generation process and eliminating the need for discrete retry cycles
3Loss of energy
If the recommendation system provides diverse and coherent recommendations, then loss of energy is reduced, but device complexity increases
Solution Approach 1:
The system merges multiple objective functions (coherence, diversity, external knowledge alignment) into a unified loss function that simultaneously optimizes all criteria, reducing the need for separate processing systems and minimizing computational resource waste through integrated optimization
Solution Approach 2:
The recommendation system serves multiple functions through a single model: it ensures contextual coherence, maintains diversity across recommendations, and aligns with external knowledge, thereby reducing the need for multiple specialized systems and lowering overall computing resource consumption
4Manufacturing precision
If scene graph expansion uses sequential prediction, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The scene graph expansion is segmented into sequential prediction steps where each node and edge is predicted individually in a structured sequence, allowing the model to focus computational resources on each specific prediction task and improve accuracy through step-by-step refinement
Data Source
AI summary
Embodiments are disclosed for expanding a seed scene using proposals from a generative model of scene graphs. The method may include clustering subgraphs according to respective one or more maximal connected subgraphs of a scene graph. The scene graph includes a plurality of nodes and edges. The method also includes generating a scene sequence for the scene graph based on the clustered subgraphs. A first machine learning model determines a predicted node in response to receiving the scene sequence. A second machine learning model determines a predicted edge in response to receiving the scene sequence and the predicted node. A scene graph is output according to the predicted node and the predicted edge.