Scene Graph Generation With Dense Relational Embeddings
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Solution Overview
Problem
Existing scene graph generation methods face challenges due to incomplete labelling, long-tailed relationship categories, and high computational complexity, making them inefficient for tasks like image caption generation and visual question answering.
Innovation Solution
A method involving a machine learning model that calculates embedding vectors for objects and positional information, evaluates connection probabilities using a trained model, and adjusts parameters to minimize loss, employing a dense relational embedding and graph-aware queries to predict scene graphs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing scene graph generation methods are used, then scene graphs can be generated, but computational complexity becomes excessively high due to combinatorial complexity
Solution Approach 1:
The patent segments the scene graph generation task into two independent stages: object detection (using DETR to identify objects and their embeddings) and relationship prediction (using a separate MLP to predict connections). This segmentation avoids the combinatorial complexity of jointly optimizing both tasks, as each stage can be processed independently with reduced computational burden.
Solution Approach 2:
The patent extracts the relationship prediction function from the object detection process. By using DETR to first identify objects and their embeddings, then separately using an MLP to predict relationships based on these embeddings, the patent removes the computationally intensive joint optimization step while maintaining generation quality.
2Reliability
If comprehensive relationship categories are used, then scene graph completeness improves, but long-tail relationship categories reduce processing efficiency
Solution Approach 1:
The patent changes the parameter space from discrete relationship category labels to continuous relationship probability distributions. Instead of predicting specific predefined relationships, the MLP outputs probability scores for potential connections, allowing the system to handle long-tail relationships naturally through probability thresholds rather than fixed category mappings.
3Measurement precision
If detailed spatial and semantic relationships are captured, then scene graph accuracy improves, but incomplete labelling reduces generation reliability
Solution Approach 1:
The patent implements feedback through the loss function during training, which compares predicted relationship probabilities against ground truth labels. This feedback mechanism allows the model to learn from incomplete or noisy labels in the training data, gradually improving its ability to handle such cases during inference while maintaining high accuracy for complete relationships.
Data Source
AI summary
A system and method are provided for analysing image data representing an image, the image representing multiple objects in the image with respective positional information. An exemplary method includes calculating an embedding of the image data, the embedding comprising an embedding vector for each object, and encoding object information and the positional information; and evaluating a trained machine learning model on the embedding to calculate connection probabilities of pair-wise connections between each of the objects, the connection probabilities representing relationships between the objects in the image.