Semantic Mask Creation for Zero-Shot Test Image Segmentation
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Solution Overview
Problem
Unsupervised segmentation methods like Segment Anything struggle to recognize semantics within segmented regions, which is crucial for accurate measurements and quality control in manufacturing, particularly in automated optical inspection (AOI) and optical security monitoring.
Innovation Solution
A method involving creating concept and test embeddings, multiplying them to generate an attention mask, and using position information to refine a bounding box for precise segmentation, leveraging a generic segmentation model like Segment Anything (SAM) or its efficient variant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unsupervised segmentation methods like Segment Anything are used, then segmentation speed and ease of operation are improved, but semantic recognition capability deteriorates
Solution Approach 1:
The patent introduces an attention mask as an intermediary element that bridges the gap between unsupervised segmentation and semantic recognition. The attention mask is generated by multiplying concept embeddings (from masked query images) with test embeddings, creating a semantic guide that directs the segmentation model to focus on relevant regions. This intermediary structure enables the model to maintain fast unsupervised operation while incorporating semantic understanding through the attention mechanism.
2Adaptability or versatility
If a generic segmentation model is used without training, then adaptability to new tasks is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing concept embeddings from masked query images before performing the actual segmentation task. These concept embeddings capture semantic information about different object categories in advance. When segmenting test images, the model multiplies these pre-computed concept embeddings with test embeddings to generate attention masks, enabling the generic model to adapt to new segmentation tasks with high precision without requiring task-specific training.
3Manufacturing precision
If multiple processing steps are added to improve segmentation quality, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple processing functions into a unified attention mask generation mechanism. Instead of separate modules for semantic understanding, region proposal, and mask refinement, the invention combines these functions by multiplying concept embeddings with test embeddings in a single attention mechanism step. This consolidated approach maintains high segmentation quality while reducing overall system complexity compared to multi-stage architectures.
Data Source
AI summary
A method for creating a mask for segmenting at least one test image. The method includes: providing a masked query image; creating a concept embedding by extracting coding features from the masked query image using a generic segmentation model; creating a test embedding by extracting coding features from the test image by means of the generic segmentation model; multiplying the concept embedding and the test embedding to obtain an attention mask; creating an initial mask for the test image using the generic segmentation model based on the attention mask, an item of position information derived from the attention mask, and the test embedding; extracting a bounding box from the created initial mask; and creating the mask for the test image using the generic segmentation model based on the attention mask, the extracted bounding box, and the test embedding for segmenting at least one test image.