Language-Guided Self-Supervised Semantic Segmentation
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Solution Overview
Problem
Semantic segmentation in computer vision is labor-intensive due to the need for pixel-wise human annotations, which are costly and limited to predefined classes, restricting the recognition capabilities of learned models.
Innovation Solution
A computer-implemented method and system for language-guided self-supervised semantic segmentation, utilizing a pretrained vision-language model and a machine learning model to generate and align pixel embeddings through data augmentation, contrastive learning, and semantic consistency, enabling segmentation without human annotations and discovering unknown classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-wise human annotations are used for semantic segmentation, then model training accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs self-supervised learning by automatically generating pseudo-labels from the model's own predictions and aligning them with language embeddings, eliminating the need for manual pixel-wise annotations. The model learns to segment images by finding consistency between visual features and language descriptions.
Solution Approach 2:
The system pre-aligns the visual feature space with the language embedding space using contrastive loss before performing segmentation. This preliminary alignment enables the model to leverage language priors for segmentation without requiring annotated data.
2Ease of manufacture
If predefined semantic classes are used for annotations, then annotation process is simplified, but model recognition capability is limited
Solution Approach 1:
The system uses a universal language embedding space that can represent both predefined semantic classes and unknown concepts. By aligning visual features with language embeddings, the model gains the ability to recognize and segment both known and unknown objects without requiring separate annotation processes.
Solution Approach 2:
The system transitions from discrete predefined class labels to continuous language embedding spaces. This dimensional transformation allows the model to capture fine-grained semantic information and recognize concepts beyond the predefined taxonomy by leveraging the richness of language descriptions.
3Measurement precision
If more annotation data is collected to improve model performance, then segmentation quality is enhanced, but cost and complexity increase
Solution Approach 1:
The model generates its own training signals by computing contrastive loss between visual features and language embeddings. This self-supervised approach creates an automated feedback loop that continuously improves segmentation quality without requiring additional annotated data or complex data collection infrastructure.
Data Source
AI summary
A computer-implemented system and method relates to language-guided self-supervised semantic segmentation. A modified image is generated by performing data augmentation on a source image. A machine learning model generates first pixel embeddings based on the modified image. First segment embeddings are generated using the first pixel embeddings. A pretrained vision-language model generates second pixel embeddings based on the source image. Second segment embeddings are generated by applying segment contour data from the first pixel embeddings to the second pixel embeddings after the data augmentation is performed on the second pixel embeddings. Embedding consistent loss data is generated by comparing the first segment embeddings in relation to the second segment embeddings. Combined loss data is generated that includes the embedding consistent loss data. Parameters of the machine learning model are updated based on the combined loss data.