Anomaly Detection is an important problem in industrial processes. Two new subfields have recently emerged: logical anomaly detection and few-shot anomaly detection. The combined task, few-shot logical anomaly detection, has proven exceptionally difficult and highly important for industrial processes. Few-shot methods use suboptimal representations to model composition information necessary for detecting logical anomalies, and previous full-shot methods require a large training set. To solve both problems, we propose ObjectCore, a few-shot logical anomaly detection model that captures the composition information from only a few images without any category-specific information. The composition information of an image is modelled as a collection of object representations. Logical anomalies are detected using bipartite matching between object representations in the test image and object representations in the most similar support image. ObjectCore significantly improves over state-of-the-art methods on two standard benchmarks for few-shot logical anomaly detection, MVTec LOCO and CAD-SD, attaining an image-level AUROC of 80.8% and 96.5%, respectively, in the 4-shot setting. Code: https://github.com/MaticFuc/ObjectCore

Abstract Surface-anomaly detection is a critical challenge in ensuring product quality, as defects can pose safety risks and diminish product lifespan. A significant challenge in this domain is the limited availability of anomalous samples which makes training supervised models impractical. In response, unsupervised deep-learning-based methods have attracted significant attention in recent years, as they do not require anomalous samples for training. Such methods assume that during dataset curation all anomalous samples can be identified and subsequently removed from the training set. In practice, however, identifying all anomalous samples without any false negatives is rarely possible, either due to the human errors or due to the ambiguity in what is considered a defect and what is not. In this paper, we address the need to measure the robustness of the unsupervised surface-anomaly detection methods as one of the most important performance metrics. To this end, we propose a robustness measure that describes the sensitivity of an unsupervised method to the presence of anomalous data in the training set. We extensively evaluate seven well established unsupervised methods that follow different anomaly detection paradigms on four diverse datasets and analyze the results. We show that most of the analyzed methods are fairly robust to low percentages of anomalous samples in the training set, with some of them retaining the near-baseline performance even when that percentage grows fairly large.

Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map, but does not provide individual object locations and sizes, which are crucial for many applications. This is addressed by detection-based counters, which, however fall behind in the total count accuracy. Furthermore, both approaches tend to overestimate the counts in the presence of other object classes due to many false positives. We propose DAVE, a low-shot counter based on a detect-and-verify paradigm, that avoids the aforementioned issues by first generating a high-recall detection set and then verifying the detections to identify and remove the out-liers. This jointly increases the recall and precision, leading to accurate counts. DAVE outperforms the top density-based counters by ~20% in the total count MAE, it outper-forms the most recent detection-based counter by ~20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting. The code and models are available on GitHub.

Recently a growing number of works focus on machine defect detection from anomalous audio patterns. The datasets for the machine audio domain are scarce and recent methods that perform well on benchmarks such as DCASE2020 Task 2, rely on auxiliary information such as annotated data from other training classes in the domain to extract information that can be used in deep-learning classification-based anomaly detection approaches. However, in practical scenarios, annotated data from the same domain may not be readily available so annotation-free methods that can learn appropriate audio representations from unannotated data are needed. We propose AudDSR, a simulation-based anomaly detection method that learns to detect anomalies without additional annotated data and instead focuses on a discrete feature space sampling method for an anomaly simulation process. AudDSR outperforms competing methods that do not rely on annotated data on the DCASE2020 anomalous sound detection benchmark and even matches the performance of some methods that utilize additional annotation information.

Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appearance aggregation. Due to potentially diverse object appearances, the existing approaches often lead to overgeneralization and false positive detections. Furthermore, the best-performing methods train object localization by a surrogate loss, that predicts a unit Gaussian at each object center. This loss is sensitive to annotation error, hyperparameters and does not directly optimize the detection task, leading to suboptimal counts. We introduce GeCo, a novel low-shot counter that achieves accurate object detection, segmentation, and count estimation in a unified architecture. GeCo robustly generalizes the prototypes across objects appearances through a novel dense object query formulation. In addition, a novel counting loss is proposed, that directly optimizes the detection task and avoids the issues of the standard surrogate loss. GeCo surpasses the leading few-shot detection-based counters by $\sim$25\% in the total count MAE, achieves superior detection accuracy and sets a new solid state-of-the-art result across all low-shot counting setups. The code will be available on GitHub.

RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporation of 3D information. Existing approaches that employ point-cloud backbones suffer from suboptimal representations and reduced applicability due to slow processing. Re-training RGB backbones, designed for faster dense input processing, on industrial depth datasets is hindered by the limited availability of sufficiently large datasets. We make several contributions to address these challenges. (i) We propose a novel Depth-Aware Discrete Autoencoder (DADA) architecture, that enables learning a general discrete latent space that jointly models RGB and 3D data for 3D surface anomaly detection. (ii) We tackle the lack of diverse industrial depth datasets by introducing a simulation process for learning informative depth features in the depth encoder. (iii) We propose a new surface anomaly detection method 3DSR, which outperforms all existing state-of-theart on the challenging MVTec3D anomaly detection benchmark, both in terms of accuracy and processing speed. The experimental results validate the effectiveness and efficiency of our approach, highlighting the potential of utilizing depth information for improved surface anomaly detection. Code is available at: https://github.com/VitjanZ/3DSR

We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching them with image features to infer the object counts. Existing methods extract queries by feature pooling which neglects the shape information (e.g., size and aspect) and leads to a reduced object localization accuracy and count estimates.We propose a Low-shot Object Counting network with iterative prototype Adaptation (LOCA). Our main contribution is the new object prototype extraction module, which iteratively fuses the exemplar shape and appearance information with image features. The module is easily adapted to zero-shot scenarios, enabling LOCA to cover the entire spectrum of low-shot counting problems. LOCA outperforms all recent state-of-the-art methods on FSC147 benchmark by 20-30% in RMSE on one-shot and few-shot and achieves state-of-the-art on zero-shot scenarios, while demonstrating better generalization capabilities. The code and models are available here: https://github.com/djukicn/loca.

Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a discriminatively trained reconstruction anomaly embedding model (DRÆM). The proposed method learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. The method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations. On the challenging MVTec anomaly detection dataset, DRÆM outperforms the current state-of-the-art unsupervised methods by a large margin and even de-livers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy. Code at github.com/VitjanZ/DRAEM.