Scene Graph Generation Using Edge Proposals and Spatial Message Passing
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Solution Overview
Problem
The computational complexity and inefficiency of traditional scene graph generation methods, particularly due to the quadratic number of potential edges and intensive relationship detection between object pairs, hinder their scalability and effectiveness in computer vision tasks.
Innovation Solution
A method utilizing an edge proposal neural network and a Graph Neural Network with spatial message passing (GNN-SMP) for efficient scene graph generation, involving feature extraction, object detection, un-labelled graph creation, and optimized edge classification to generate scene graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object pair relationship detection is used, then scene graph classification can be performed, but computational complexity increases quadratically with the number of objects
Solution Approach 1:
The patent segments the edge representation generation process into two stages: first generating candidate edges using a neural network that processes object pairs, then filtering and selecting final edges. This segmentation reduces the computational burden by not evaluating all possible object pairs exhaustively, instead using a learned model to generate only the most relevant edges, thereby reducing quadratic complexity while maintaining classification accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-processing object features and spatial relationships before generating edge representations. The system extracts features from detected objects and pre-computes spatial relationships, then feeds this processed information into the edge generation neural network. This preliminary processing enables more efficient edge candidate generation and reduces the computational load during the main classification task.
2Measurement precision
If all potential edges are considered in scene graph generation, then comprehensive relationship detection is achieved, but processing time increases significantly
Solution Approach 1:
The patent applies partial action by generating only the necessary subset of edges rather than processing all possible object pairs. The neural network generates edge candidates based on learned patterns from training data, which filters out unnecessary edges and focuses computation on the most likely relationships. This partial processing approach maintains sufficient relationship detection completeness while significantly reducing processing time compared to exhaustive evaluation.
Solution Approach 2:
The patent changes the parameter of edge generation from exhaustive enumeration to neural network-based sampling. Instead of systematically evaluating all N(N-1)/2 potential edges where N is the number of objects, the system uses a neural network that transforms object features and spatial relationships into probability distributions over possible edges. This parameter change from deterministic exhaustive search to probabilistic neural generation reduces processing time while maintaining detection precision through the learned edge probability model.
Data Source
AI summary
The disclosure generally relates to scene graph generation. Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph and has several applications including image retrieval, action recognition, visual question answering, autonomous driving, robotics. However, to leverage scene graphs, computationally efficient scene graph generation methods are required, which is very challenging to generate due presence of a quadratic number of potential edges and computationally intensive/non-scalable techniques for detecting the relationship between each object pair using the traditional approach. The disclosure proposes a combination of edge proposal neural network and the Graph neural network with spatial message passing (GNN-SMP) along with several techniques including a feature extraction technique, object detection technique, un-labelled graph generation technique and a scene graph generation technique to generate scene graphs.