Generalist Anomaly Detection Model with Few-shot Sample Prompts

Основные понятия

The author proposes a Generalist Anomaly Detection (GAD) model, InCTRL, that utilizes few-shot normal images as sample prompts to detect anomalies across diverse datasets without further training.

Аннотация

The paper introduces InCTRL, a GAD model trained on auxiliary data to detect anomalies in various domains using few-shot normal image prompts. It outperforms competing methods on industrial defects, medical anomalies, and semantic anomalies in both one-vs-all and multi-class settings.
The approach involves learning an in-context residual model for GAD named InCTRL, which discriminates anomalies from normal samples based on residuals between query images and few-shot normal sample prompts. This enables the model to generalize across different datasets without additional training.
Experiments on nine diverse AD datasets establish a GAD benchmark covering industrial defect detection, medical image anomaly detection, and semantic anomaly detection. InCTRL significantly outperforms state-of-the-art methods across all datasets.
InCTRL's success is attributed to its ability to capture fine-grained residuals at both patch and image levels while incorporating text prompt-guided prior knowledge. The model demonstrates remarkable generalization capabilities across various anomaly detection tasks.

Статистика

False positive rate: 0.733±0.000
True positive rate: 0.946±0.000
Baseline AUROC: 0.855±0.000

Цитаты

"InCTRL significantly outperforms state-of-the-art competing methods."
"Our results show that InCTRL achieves the best performance."

Ключевые выводы из

Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

by Jiawen Zhu,G... в arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06495.pdf

Дополнительные вопросы

How can the concept of in-context residual learning be applied to other computer vision tasks

In-context residual learning, as demonstrated in the study on Generalist Anomaly Detection (GAD), can be applied to various other computer vision tasks that involve anomaly detection or feature extraction. One potential application could be in object recognition tasks where anomalies need to be identified within a dataset. By leveraging in-context residual learning, models can learn to capture discrepancies between normal and abnormal instances at both patch-level and image-level resolutions. This approach can enhance the model's ability to detect anomalies across different datasets without requiring specific training on each new dataset.

What are the potential limitations or challenges of using few-shot sample prompts for anomaly detection

Using few-shot sample prompts for anomaly detection may pose certain limitations and challenges. One limitation is the reliance on a small number of normal images as prompts, which may not fully represent the diversity of normal patterns present in a dataset. This could lead to biases or inaccuracies in anomaly detection if the few-shot samples do not adequately cover all variations of normal data distribution. Additionally, there might be difficulties in selecting representative few-shot samples that effectively capture normal patterns across different domains or applications. The generalization capability of models trained with few-shot sample prompts may also vary depending on the complexity and uniqueness of anomalies present in diverse datasets.

How might the findings of this study impact the development of future anomaly detection models

The findings from this study have significant implications for the development of future anomaly detection models by introducing a novel approach - InCTRL - that focuses on Generalist Anomaly Detection (GAD). The success of InCTRL highlights the importance of training one single model capable of detecting anomalies across diverse datasets without domain-specific fine-tuning. This approach opens up possibilities for creating more generalized anomaly detection systems that can adapt to new datasets without extensive retraining efforts.
Future anomaly detection models could benefit from incorporating similar strategies like in-context residual learning and utilizing few-shot sample prompts for enhanced generalization capabilities. By addressing challenges related to cross-domain anomaly detection, researchers can advance towards developing more robust and versatile anomaly detection solutions applicable across various real-world scenarios.