Semantic Segmentation Training With Multimodal Image-Text Features
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Solution Overview
Problem
Existing semantic segmentation models trained solely on visual features from sample images suffer from low information utilization, leading to inefficient training and poor performance due to the loss of valuable information, necessitating a higher number of training samples.
Innovation Solution
The method integrates visual features with text features generated from language description text for sample images, fusing them to create multimodal features and using these to train the model, enhancing information utilization and improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the semantic segmentation model is trained solely on visual features from sample images, then the training process is simpler, but the information utilization is low leading to poor performance and inefficient training
Solution Approach 1:
The patent combines visual features and text features into a unified multimodal feature representation. The visual feature extraction module processes images while the text feature extraction module processes language descriptions, and both feature streams are merged to train the semantic segmentation model, thereby reducing information loss and improving performance.
Solution Approach 2:
The patent creates composite feature representations by integrating heterogeneous data types (visual and text features) into a unified multimodal feature space. This composite approach allows the model to leverage complementary information from different modalities, improving information utilization without excessive complexity increase.
2Reliability
If more training samples are used to compensate for low information utilization, then the model performance can be improved, but the training time and computational resources increase
Solution Approach 1:
By merging visual and text features, the model achieves better performance with fewer training samples. The complementary information from text descriptions helps the model learn more efficiently, reducing the need for large quantities of training data and thereby shortening training time.
Solution Approach 2:
The text feature extraction module acts as an intermediary that provides additional semantic information about the images. This intermediary text modality guides the visual feature learning process, enabling the model to achieve better performance with reduced training requirements.
3Loss of information
If the model processes both visual and text features to create multimodal features, then the information utilization is enhanced, but the processing complexity increases
Solution Approach 1:
The patent segments the feature processing into distinct modules: a visual feature extraction module for processing images and a text feature extraction module for processing language descriptions. This segmentation allows each module to specialize in its modality while maintaining overall processing efficiency through modular architecture.
Solution Approach 2:
The patent performs preliminary feature extraction separately for visual and text inputs before fusing them. This preliminary processing organizes the data in advance, making the subsequent fusion and model training more efficient despite the increased overall processing requirements.
Data Source
AI summary
A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a sample image, and extracting visual image features corresponding to the sample image by a semantic segmentation model to be trained; processing the sample image to obtain a text image feature corresponding to the sample image, the text image feature being an image feature generated from language description text for the sample image; fusing the visual image features with the text image feature to obtain multimodal features, and performing image segmentation prediction based on the multimodal features to obtain a target loss; and training the semantic segmentation model to be trained based on the target loss to obtain a target semantic segmentation model.