Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

Core Concepts

A novel semi-supervised anomaly detection algorithm, SemiREST, that achieves state-of-the-art performance while requiring significantly less annotation effort compared to fully-supervised methods.

Abstract

The paper proposes a novel semi-supervised anomaly detection algorithm called "SemiREST" that outperforms state-of-the-art methods on popular benchmarks while requiring much less annotation effort.
Key highlights:

SemiREST treats anomaly detection as a block-wise classification problem instead of pixel-wise segmentation, reducing the required annotation effort.
It further reduces the annotation cost by using bounding box labels instead of full pixel-level annotations.
SemiREST leverages a semi-supervised learning scheme, customized for the residual-based anomaly detection, to effectively utilize the unlabeled data.
Extensive experiments on MVTec-AD, BTAD, and KolektorSDD2 datasets show that SemiREST outperforms state-of-the-art methods in both unsupervised and supervised settings.
Notably, with only bounding box annotations, SemiREST still outperforms fully-supervised state-of-the-art methods.
The proposed approach demonstrates the effectiveness of combining novel semi-supervised learning techniques with efficient annotation strategies for achieving state-of-the-art anomaly detection performance.

Stats

"Anomaly Detection (AD) is challenging as usually only the normal samples are seen during training and the detector needs to discover anomalies on-the-fly."
"In some particular AD tasks, a few anomalous samples are labeled manually for achieving higher accuracy. However, this performance gain is at the cost of considerable annotation efforts, which can be intractable in many practical scenarios."

Quotes

"Compared with the lack of training anomalies, a more realistic problem to address is the limited time budget for labeling the newly-obtained anomalous images."
"The productive utilization of the unlabeled information reduces the performance drop from full supervision."

Key Insights Distilled From

Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

by Hanxi Li,Jin... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2306.03492.pdf

Deeper Inquiries

How can the proposed semi-supervised learning scheme be extended to other computer vision tasks beyond anomaly detection

The proposed semi-supervised learning scheme can be extended to other computer vision tasks beyond anomaly detection by leveraging weak supervision and innovative data augmentation techniques. One way to extend this scheme is to apply it to tasks like image classification, object detection, and semantic segmentation. By incorporating weak labels, such as bounding boxes or partial annotations, the model can learn from both labeled and unlabeled data, improving its performance without the need for extensive manual annotation. Additionally, customized data augmentation methods, similar to the ResMix-Match algorithm used in the proposed scheme, can be tailored to specific tasks to enhance the model's ability to learn from limited labeled data. This approach can lead to more efficient and effective training of computer vision models across various applications.

What are the potential limitations of using bounding box annotations for anomaly detection, and how can they be addressed

Using bounding box annotations for anomaly detection may have some limitations that need to be addressed. One potential limitation is the lack of granularity in the annotations, as bounding boxes provide information about the location of anomalies but not the specific pixel-level details. This can lead to challenges in accurately identifying and classifying anomalies, especially in cases where anomalies are small or intricate. To address this limitation, additional techniques such as instance segmentation or fine-grained labeling within bounding boxes can be employed to provide more detailed information about the anomalies. Another limitation is the potential for misalignment between the bounding boxes and the actual anomaly regions, which can impact the model's performance. To mitigate this, techniques like data augmentation, spatial transformation, or ensemble learning can be used to improve the alignment and robustness of the model to variations in the annotations.

What other types of weak supervision, beyond bounding boxes, could be leveraged to further reduce the annotation cost for anomaly detection

Beyond bounding boxes, there are several other types of weak supervision that could be leveraged to further reduce the annotation cost for anomaly detection. One approach is to use point annotations, where specific points or landmarks within an image are labeled as anomalous. This can provide more precise localization information compared to bounding boxes and can be particularly useful for detecting anomalies in complex or irregular shapes. Another type of weak supervision is partial annotations, where only a subset of anomalies in an image are labeled, leaving the rest unlabeled. This can help the model learn to generalize to unseen anomalies and improve its robustness. Additionally, weak supervision techniques like image-level labels, scribbles, or partial masks can also be explored to provide varying levels of supervision while reducing annotation efforts. By incorporating a combination of these weak supervision strategies, the annotation cost for anomaly detection can be further minimized while maintaining high detection accuracy.