Scene Graph Generation Using Gradient Reversal Layers
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Solution Overview
Problem
Conventional scene graph generation techniques rely on expensive labeled data, making it challenging to perform reliably on real data due to domain gaps, especially when trained using synthetic data.
Innovation Solution
The approach decomposes domain gaps into appearance, label, and prediction discrepancies, using gradient reversal layers and pseudo-statistics-based self-learning to align latent and output distributions, and generate accurate labels for synthetic data, enabling scene graph generation for unlabeled real images across different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If synthetic data is used for training scene graph generation models, then labeling cost is reduced, but performance reliability deteriorates due to domain gap
Solution Approach 1:
The patent introduces gradient reversal layers as an intermediary mechanism that mediates between synthetic and real data domains. These layers enable domain adaptation by reversing gradients during training to prevent the model from learning domain-specific features, thereby reducing the domain gap while maintaining the cost benefits of synthetic data training
Solution Approach 2:
The patent modifies model parameters dynamically during training by introducing domain adaptation parameters and using pseudo-statistics to adjust feature distributions. This allows the model to adapt from synthetic data domain to real data domain, improving reliability while maintaining synthetic data training benefits
2Reliability
If labeled real data is used for training, then performance reliability is improved, but cost and time consumption increase
Solution Approach 1:
The patent implements self-learning mechanisms where the model generates its own training signals through pseudo-statistics computation. By automatically generating pseudo-labels and computing pseudo-statistics from unlabeled real data, the system serves itself without requiring manual annotation, thereby maintaining reliability while eliminating annotation time costs
Solution Approach 2:
The patent introduces feedback loops where pseudo-statistics computed from model predictions are fed back into the training process. This feedback mechanism allows the model to continuously improve its performance on unlabeled real data, achieving reliability improvement without the time cost of manual labeling
3Adaptability or versatility
If domain adaptation techniques are applied, then domain gap is reduced, but model complexity increases
Solution Approach 1:
The patent segments the domain adaptation problem into distinct components: appearance domain adaptation handled by gradient reversal layers and predicate domain adaptation handled by pseudo-statistics alignment. This segmentation allows each component to be addressed with targeted techniques, reducing overall model complexity while maintaining domain adaptability
Data Source
AI summary
Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.