Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study

• URL: http://arxiv.org/abs/2401.08686v1
• Date: Sat, 13 Jan 2024 03:09:47 GMT
• Title: Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study
• Authors: Andr\'e Luiz B. Vieira e Silva, Francisco Sim\~oes, Danny Kowerko, Tobias Schlosser, Felipe Battisti, Veronica Teichrieb
• Abstract summary: This paper proposes a DifferNet-based solution enhanced with attention modules utilizing SENet and CBAM as backbone - AttentDifferNet. In comparison to the current state of the art, it is shown that AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quantitative as well as qualitative evaluation.
• Score: 2.2942964892621807
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Within (semi-)automated visual inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To not only alleviate this issue but to furthermore advance the current state of the art in unsupervised visual inspection, this contribution proposes a DifferNet-based solution enhanced with attention modules utilizing SENet and CBAM as backbone - AttentDifferNet - to improve the detection and classification capabilities on three different visual inspection and anomaly detection datasets: MVTec AD, InsPLAD-fault, and Semiconductor Wafer. In comparison to the current state of the art, it is shown that AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quantitative as well as qualitative evaluation, indicated by a general improvement in AUC of 94.34 vs. 92.46, 96.67 vs. 94.69, and 90.20 vs. 88.74%. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for anomaly detection.

Related papers

• Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [1.0358639819750703]
  In unsupervised anomaly detection (UAD) research, it is necessary to develop a computationally efficient and scalable solution. We revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. We propose Feature Attenuation of Defective Representation (FADeR) that only employs two layers which attenuates feature information of anomaly reconstruction.
  arXiv  Detail & Related papers  (2024-07-05T15:44:53Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects [4.487908181569429]
  We present a novel self-supervised learning algorithm designed to derive an optimal encoder by tackling the renowned jigsaw puzzle. Our approach involves dividing the target image into nine patches, tasking the encoder with predicting the relative position relationships between any two patches to extract rich semantics.
  arXiv  Detail & Related papers  (2023-11-11T08:01:40Z)
• Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [2.2942964892621807]
  We propose a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection. Our evaluation shows an average improvement - compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC.
  arXiv  Detail & Related papers  (2023-11-05T19:48:50Z)
• ORA3D: Overlap Region Aware Multi-view 3D Object Detection [11.58746596768273]
  Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly. We propose using the following two main modules: (1) Stereo Disparity Estimation for Weak Depth Supervision and (2) Adrial Overlap Region Discriversaminator. Our proposed method outperforms current state-of-the-art models, i.e., DETR3D and BEVDet.
  arXiv  Detail & Related papers  (2022-07-02T15:28:44Z)
• LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection [0.0]
  We propose a novel model called Layer-wise External Attention Network (LEA-Net) for efficient image anomaly detection. Our strategy is as follows: (i) Prior knowledge about anomalies is represented as the anomaly map generated by unsupervised learning of normal instances, (ii) The anomaly map is translated to an attention map by the external network, (iii) The attention map is then incorporated into intermediate layers of the anomaly detection network.
  arXiv  Detail & Related papers  (2021-09-12T11:38:04Z)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)

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.