Image Anomaly Detection Using Text-Based Feature Embeddings
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Solution Overview
Problem
Conventional anomaly detection models are time-consuming and labor-intensive due to the need for extensive labeled data and frequent retraining, and they become outdated quickly in dynamic environments where anomalies evolve.
Innovation Solution
A text-based approach using pre-trained vision-language models for zero-shot, one-shot, or few-shot anomaly detection, where textual descriptions of normal and anomalous conditions are transformed into a shared embedding space, allowing for efficient adaptation to new contexts without extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional anomaly detection models are trained using labeled datasets containing both normal and abnormal images, then the model can effectively detect anomalies, but the training process becomes time-consuming and labor-intensive due to the need for extensive labeled data
Solution Approach 1:
The patent applies preliminary action by using a pre-trained vision-language model that has already learned general visual and linguistic representations before being adapted for anomaly detection. This pre-training phase performs the heavy lifting of learning fundamental patterns, so when the model is later fine-tuned for specific anomaly detection tasks, it requires significantly less labeled data and training time while still achieving high detection accuracy
Solution Approach 2:
The patent introduces text descriptions as an intermediary element between the image input and anomaly detection output. The vision-language model processes both images and their textual descriptions together, using the text as a mediator to guide the anomaly detection process. This intermediary approach allows the model to leverage linguistic knowledge to improve detection accuracy without requiring extensive labeled anomaly images
2Measurement precision
If conventional anomaly detection models are trained with labeled data, then the model achieves initial accuracy, but the model rapidly becomes outdated in dynamic environments where anomalies evolve over time, requiring regular retraining
Solution Approach 1:
The patent applies dynamics by enabling the model to adapt to evolving anomaly patterns through continuous fine-tuning with minimal new labeled data. The vision-language model's architecture allows it to dynamically update its understanding of what constitutes normal versus anomalous patterns as new data becomes available, rather than becoming stagnant after initial training
Solution Approach 2:
By using text descriptions as an intermediary, the model can incorporate evolving knowledge about anomalies through natural language updates to the text descriptions. This allows the model to adapt to new anomaly types or patterns by updating the linguistic guidance rather than requiring complete retraining on large datasets of new anomaly images
3Measurement precision
If extensive labeled data is used for training anomaly detection models, then model accuracy is maintained, but the amount of labor and resources required for data labeling and model updates increases significantly
Solution Approach 1:
The patent applies partial action by using only a small subset of labeled data (either a single image or a few images) for fine-tuning the vision-language model, rather than requiring extensive labeled datasets. This partial approach is sufficient to adapt the pre-trained model to specific anomaly detection tasks while dramatically reducing the labeling effort and resources required
Solution Approach 2:
The text descriptions serve as an intermediary that carries the essential information about anomaly patterns without requiring numerous labeled images. By encoding anomaly characteristics in natural language, the system reduces the burden of data labeling while maintaining the ability to train effective anomaly detection models
Data Source
AI summary
Method and apparatus for image processing. A plurality of positive text exemplars is processed to generate a set of normal features using a trained model. A plurality of negative text exemplars is processed to generate a set of anomaly features using the trained model. A query image depicting an object is received. A query image feature for the query image is generated using the trained model. An anomaly score for the query image is generated based at least in part on determining one or more distances between the query image feature and one or more normal features of the set of normal features, and determining one or more distances between the query image feature and one or more anomaly features of the set of anomaly features.