End-to-End Scene Graph Generation for Spatially Refined Classification
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Solution Overview
Problem
Existing methods for generating scene graphs from images typically determine object types and spatial relationships sequentially, rather than simultaneously, leading to inaccuracies in object classification.
Innovation Solution
An end-to-end scene graph generator using a single neural network that integrates object detection and relationship estimation stages to jointly determine object types and spatial relationships, leveraging deep learning to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If object types and spatial relationships are determined sequentially, then the process is simpler and more modular, but the object classification accuracy deteriorates
Solution Approach 1:
The patent merges the object detection module and relationship estimation module into a single integrated neural network. The object detection layer and relationship estimation layer share common features and are trained jointly, allowing the model to simultaneously learn both object classification and spatial relationships. This integration enables the relationship information to feedback and refine object type predictions, resolving the technical contradiction by improving accuracy without proportionally increasing complexity.
2Measurement precision
If object types and spatial relationships are determined simultaneously using an integrated neural network, then object classification accuracy is improved, but the device complexity increases
Solution Approach 1:
The integrated neural network is segmented into distinct functional layers: an object detection layer for identifying objects and their types, a relationship estimation layer for determining spatial relationships, and a feedback mechanism that connects them. This segmentation allows each layer to specialize in its function while maintaining overall integration, managing complexity through structured modularity within the unified model.
Solution Approach 2:
The neural network is designed with multi-functionality, where the same model structure performs both object detection and relationship estimation tasks. The shared feature extraction and joint training approach enable a single model to handle multiple functions that would traditionally require separate systems, improving efficiency while controlling complexity through unified architecture.
3Adaptability or versatility
If a single neural network is used for end-to-end scene graph generation, then the integration of object detection and relationship estimation is improved, but the training difficulty increases
Solution Approach 1:
The patent employs preliminary action by pre-defining the network architecture with specialized layers for object detection and relationship estimation before training. The loss function is pre-configured to jointly optimize both objectives, and the training process uses pre-computed ground truth labels for both object types and relationships. This preparation reduces training difficulty by establishing a clear optimization target and structured learning framework from the outset.
Solution Approach 2:
The model incorporates feedback mechanisms where relationship estimation results are used to refine object type predictions, and updated object predictions feed back into relationship re-estimation. This iterative feedback loop during training allows the model to progressively improve both object classification and relationship detection, making the complex training process more manageable through continuous refinement rather than single-pass optimization.
Data Source
AI summary
In one implementation, a method of generating a scene graph includes generating the scene graph using an end-to-end scene graph generator comprising an integrated neural network. For example, in various implementations, the method includes obtaining an image representing a plurality of objects. The method includes determining a relationship vector indicating spatial relationships between a particular object of the plurality of objects and others of the plurality of objects. The method includes determining, based on the relationship vector, an object type of the particular object.