Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy

• URL: http://arxiv.org/abs/2310.04010v2
• Date: Fri, 10 Nov 2023 00:50:54 GMT
• Title: Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy
• Authors: YeongHyeon Park, Sungho Kang, Myung Jin Kim, Yeonho Lee, Hyeong Seok Kim, Juneho Yi
• Abstract summary: We propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR) EAR features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Our approach achieves a high anomaly detection performance without any change of the neural network structure.
• Score: 1.0358639819750703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalous patterns. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: 1) time-consuming inference due to multiple masking, 2) output inconsistency by random masking strategy, and 3) inaccurate reconstruction of normal patterns when the masked area is large. Motivated by this, we propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the UAD performance than emptying those regions by binary masking, thereby overcomes issue 3. Our approach achieves a high UAD performance without any change of the neural network structure. Thus, we suggest that EAR be adopted in various manufacturing industries as a practically deployable solution.

Related papers

• Perturb, Attend, Detect and Localize (PADL): Robust Proactive Image Defense [5.150608040339816]
  We introduce PADL, a new solution able to generate image-specific perturbations using a symmetric scheme of encoding and decoding based on cross-attention. Our method generalizes to a range of unseen models with diverse architectural designs, such as StarGANv2, BlendGAN, DiffAE, StableDiffusion and StableDiffusionXL.
  arXiv  Detail & Related papers  (2024-09-26T15:16:32Z)
• Attention-Guided Perturbation for Unsupervised Image Anomaly Detection [39.48326211958042]
  We present a reconstruction framework named Attention-Guided Pertuation Network (AGPNet) It learns to add perturbation noise with an attention mask, for accurate unsupervised anomaly detection. Our framework obtains leading anomaly detection performance under various setups including few-shot, one-class, and multi-class setups.
  arXiv  Detail & Related papers  (2024-08-14T12:12:43Z)
• 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)
• DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery [71.6345505427213]
  DPMesh is an innovative framework for occluded human mesh recovery. It capitalizes on the profound diffusion prior about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model.
  arXiv  Detail & Related papers  (2024-04-01T18:59:13Z)
• 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)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Exploring the Relationship between Samples and Masks for Robust Defect Localization [1.90365714903665]
  This paper proposes a one-stage framework that detects defective patterns directly without the modeling process. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping. Results show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability.
  arXiv  Detail & Related papers  (2023-06-19T06:41:19Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [89.49600182243306]
  We reformulate the reconstruction process using a diffusion model into a noise-to-norm paradigm. We propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Self-Supervised Masking for Unsupervised Anomaly Detection and Localization [22.671913403500728]
  We propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization. SSM not only enhances the training of the inpainting network but also leads to great improvement in the efficiency of mask prediction at inference. To improve the efficiency and effectiveness of anomaly detection and localization at inference, we propose a novel progressive mask refinement approach.
  arXiv  Detail & Related papers  (2022-05-13T11:42:06Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)

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.