Semantic Embedding Fusion for Explainable Category Prediction
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Solution Overview
Problem
Current explainable autonomous driving methods insufficiently utilize semantic information and neglect dynamic correlations among categories, leading to inconsistencies and overlooked predictions.
Innovation Solution
A semantic-related learning method that fuses feature representations from captured images with semantic embeddings, using a prediction model trained on loss information to enhance category-specific representations and model performance by incorporating semantic information and dynamic correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current explainable autonomous driving methods are used, then basic classification functionality is provided, but semantic information is insufficiently utilized leading to poor discriminative representations
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary component that bridges image features and category predictions. The semantic embedding module processes textual category information and generates embeddings that are fused with image feature maps, enabling the model to utilize semantic information for improving discriminative representations without losing visual information.
Solution Approach 2:
The patent merges image feature maps with semantic embeddings through a fusion mechanism. The feature map from image encoding is combined with the semantic embedding vector to produce enhanced representations that incorporate both visual and semantic information, thereby improving the model's ability to generate accurate and explainable predictions.
2Reliability
If current methods are used, then category prediction is performed, but dynamic correlations among categories are neglected causing inconsistencies
Solution Approach 1:
The patent implements a feedback mechanism where predicted category probabilities are used to dynamically adjust the semantic embedding and feature fusion. The loss function incorporates category correlation information, and the system iteratively refines predictions by considering relationships between categories, thereby improving consistency without requiring overly complex explicit relationship modeling.
Solution Approach 2:
The patent changes the parameter space by introducing semantic embedding vectors that capture category relationships. Instead of explicitly modeling complex inter-category dependencies, the system transforms the problem into a semantic space where category relationships are encoded in the embedding vectors, simplifying the overall system while improving reliability.
3Productivity
If deep learning models are used for autonomous driving, then high efficiency is achieved, but explainability is limited creating black-box operation
Solution Approach 1:
The semantic embedding acts as an intermediary that provides interpretability bridges. By incorporating textual category information and generating semantic embeddings that can be visually represented and explained, the model maintains deep learning efficiency while providing explainable outputs that show what features led to specific predictions.
Solution Approach 2:
The patent segments the deep learning model into distinct modules: image encoding, semantic embedding generation, feature fusion, and prediction. This segmentation allows the explainable components (semantic embedding and fusion mechanisms) to be isolated and explained separately from the computational core, maintaining efficiency while providing transparency.
Data Source
AI summary
A semantic-related learning method and apparatus are provided. A captured image is encoded to generate a feature map. Multiple category information are encoded to generate multiple semantic embeddings. The feature map and the semantic embeddings are fused to generate a fused feature. The category information corresponding to the fused feature is predicted through a prediction model. The prediction model is trained based on a loss information between the predicted category information and a real information of the captured image.