Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
- URL: http://arxiv.org/abs/2306.03492v5
- Date: Thu, 11 Jul 2024 14:17:34 GMT
- Title: Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
- Authors: Hanxi Li, Jingqi Wu, Lin Yuanbo Wu, Hao Chen, Deyin Liu, Mingwen Wang, Peng Wang,
- Abstract summary: "Weakly-supervised RESidual Transformer" aims to achieve high AD accuracy while minimizing the need for extensive annotations.
We design a residual-based transformer model, termed "Positional Fast Anomaly Residuals" (PosFAR)
On the benchmark dataset MVTec-AD, our proposed WeakREST framework achieves a remarkable Average Precision (AP) of 83.0%.
- Score: 7.487975220416574
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in industrial Anomaly Detection (AD) have shown that incorporating a few anomalous samples during training can significantly boost accuracy. However, this performance improvement comes at a high cost: extensive annotation efforts, which are often impractical in real-world applications. In this work, we propose a novel framework called "Weakly-supervised RESidual Transformer" (WeakREST), which aims to achieve high AD accuracy while minimizing the need for extensive annotations. First, we reformulate the pixel-wise anomaly localization task into a block-wise classification problem. By shifting the focus to block-wise level, we can drastically reduce the amount of required annotations without compromising on the accuracy of anomaly detection Secondly, we design a residual-based transformer model, termed "Positional Fast Anomaly Residuals" (PosFAR), to classify the image blocks in real time. We further propose to label the anomalous regions using only bounding boxes or image tags as weaker labels, leading to a semi-supervised learning setting. On the benchmark dataset MVTec-AD, our proposed WeakREST framework achieves a remarkable Average Precision (AP) of 83.0%, significantly outperforming the previous best result of 75.8% in the unsupervised setting. In the supervised AD setting, WeakREST further improves performance, attaining an AP of 87.6% compared to the previous best of 78.6%. Notably, even when utilizing weaker labels based on bounding boxes, WeakREST surpasses recent leading methods that rely on pixel-wise supervision, achieving an AP of 87.1% against the prior best of 78.6% on MVTec-AD. This precision advantage is also consistently observed on other well-known AD datasets, such as BTAD and KSDD2.
Related papers
- Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly
Detection [64.21963650519312]
Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality.
We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space.
Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types.
arXiv Detail & Related papers (2023-10-01T21:24:05Z) - REB: Reducing Biases in Representation for Industrial Anomaly Detection [16.550844182346314]
We propose Reducing Biases (REB) in representation by considering the domain bias and building a self-supervised learning task for better domain adaption.
We also propose a local-density KNN (LDKNN) to reduce the local density bias in the feature space and obtain effective anomaly detection.
The proposed REB method achieves a promising result of 99.5% Im.AUROC on the widely used MVTec AD, with smaller backbone networks such as Vgg11 and Resnet18.
arXiv Detail & Related papers (2023-08-24T05:32:29Z) - Unsupervised Domain Adaptive Salient Object Detection Through
Uncertainty-Aware Pseudo-Label Learning [104.00026716576546]
We propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations.
We show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets.
arXiv Detail & Related papers (2022-02-26T16:03:55Z) - Mean-Shifted Contrastive Loss for Anomaly Detection [34.97652735163338]
We propose a new loss function which can overcome failure modes of both center-loss and contrastive-loss methods.
Our improvements yield a new anomaly detection approach, based on $textitMean-Shifted Contrastive Loss$.
Our method achieves state-of-the-art anomaly detection performance on multiple benchmarks including $97.5%$ ROC-AUC.
arXiv Detail & Related papers (2021-06-07T17:58:03Z) - Regressive Domain Adaptation for Unsupervised Keypoint Detection [67.2950306888855]
Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain.
We present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection.
Our method brings large improvement by 8% to 11% in terms of PCK on different datasets.
arXiv Detail & Related papers (2021-03-10T16:45:22Z) - Combining GANs and AutoEncoders for Efficient Anomaly Detection [0.0]
CBiGAN is a novel method for anomaly detection in images.
Our model exhibits fairly good modeling power and reconstruction consistency capability.
Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin.
arXiv Detail & Related papers (2020-11-16T17:07:55Z) - SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector.
It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection.
Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
arXiv Detail & Related papers (2020-07-26T12:32:38Z) - Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic
Segmentation [63.75774438196315]
Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data.
Most existing methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model.
We propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation.
arXiv Detail & Related papers (2020-04-19T15:30:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.