Unified Neural Network Model for Multi-Domain Semantic Segmentation
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Solution Overview
Problem
Existing semantic segmentation models are limited by their reliance on single datasets, which leads to ineffective training due to label conflicts and lack of relationships between different label spaces, resulting in suboptimal performance on diverse and varied data.
Innovation Solution
A method is developed to combine data from multiple datasets with different label spaces, identifying relationships between labels using cosine similarity, and training a unified neural network model with a class relational binary cross-entropy loss to generate a unified model that can handle diverse and varied data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple datasets with different label spaces are combined for training, then training efficacy and model performance on diverse data are improved, but label conflicts and inconsistencies between different label spaces cause training instability and reduced reliability
Solution Approach 1:
The patent introduces a label alignment module as an intermediary component that mediates between different label spaces. This module maps labels from multiple datasets with different label spaces into a unified label space, resolving label conflicts and enabling stable training across diverse datasets without direct contradiction between different labeling conventions.
Solution Approach 2:
The patent changes the parameter representation by transforming labels from different label spaces into a unified label space through parameter mapping. This involves reparameterizing the label space to accommodate multiple datasets, using techniques such as label smoothing and probability distribution adjustment to maintain training stability while incorporating diverse label perspectives.
2Ease of manufacture
If labels from multiple datasets are directly combined without relationship identification, then training simplicity is maintained, but loss of information about inter-class relationships reduces measurement precision and training effectiveness
Solution Approach 1:
The patent performs preliminary action by pre-identifying and establishing relationships between labels from different datasets before the main training process. This includes pre-computing label similarity matrices, hierarchical relationships, and semantic connections, which are then utilized during training to guide the learning process and maintain precision without complicating the overall training procedure.
Solution Approach 2:
The patent implements feedback mechanisms where the identified label relationships are continuously used to adjust and refine the training process. The system feedbacks label relationship information back into the training loop, allowing the model to leverage inter-class relationships for improved measurement precision while maintaining training simplicity through automated relationship utilization.
Data Source
AI summary
Methods and systems for training a model include combining data from multiple datasets, the datasets having different respective label spaces. Relationships between labels in the different label spaces are identified. A unified neural network model is trained, using the combined data and the identified relationships to generate a unified model, with a class relational binary cross-entropy loss.