InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation

• URL: http://arxiv.org/abs/2604.04632v1
• Date: Mon, 06 Apr 2026 12:30:46 GMT
• Title: InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation
• Abstract summary: InCTRLv2 introduces two new perspectives of anomaly perception under a dual-branch framework.<n>This is accomplished by two novel modules upon InCTRL: Discriminative Anomaly Score Learning (DASL) and One-class Anomaly Score Learning (OASL)<n>Experiments on ten AD datasets demonstrate that InCTRLv2 achieves SotA performance in both anomaly detection and segmentation tasks across various settings.
• Score: 40.431502791639936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent anomaly detection (AD) methods have made substantial progress in recognizing abnormal patterns within specific domains, most of them are specialist models that are trained on large training samples from a specific target dataset, struggling to generalize to unseen datasets. To address this limitation, the paradigm of Generalist Anomaly Detection (GAD) has emerged in recent years, aiming to learn a single generalist model to detect anomalies across diverse domains without retraining. To this end, this work introduces InCTRLv2, a novel few-shot Generalist Anomaly Detection and Segmentation (GADS) framework that significantly extends our previously proposed GAD model, InCTRL. Building on the idea of learning in-context residuals with few-shot normal examples to detect anomalies as in InCTRL, InCTRLv2 introduces two new, complementary perspectives of anomaly perception under a dual-branch framework. This is accomplished by two novel modules upon InCTRL: i) Discriminative Anomaly Score Learning (DASL) with both normal and abnormal data in the main branch, which learns a semantic-guided abnormality and normality space that supports the classification of query samples from both the abnormality and normality perspectives; and ii) One-class Anomaly Score Learning (OASL) using only the normal data, which learns generalized normality patterns in a semantic space via an auxiliary branch, focusing on detecting anomalies through the lens of normality solely. Both branches are guided by rich visual-text semantic priors encoded by large-scale vision-language models. Together, they offer a dual semantic perspective for AD: one emphasizes normal-abnormal discriminations, while the other emphasizes normality-deviated semantics. Extensive experiments on ten AD datasets demonstrate that InCTRLv2 achieves SotA performance in both anomaly detection and segmentation tasks across various settings.

Related papers

• A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection [2.893006778402251]
  We propose a multidirectional meta-learning algorithm to learn the manifold of the normal data.<n>At the inner level, the model aims to learn the manifold of the normal data.<n>At the outer level, the model is meta-tuned with a few anomaly samples.
  arXiv  Detail & Related papers  (2026-01-27T17:39:11Z)
• Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time [60.341117019125214]
  We propose a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns in graph anomaly detection (GAD)<n>To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level.<n>Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
  arXiv  Detail & Related papers  (2025-11-10T12:10:05Z)
• Normal-Abnormal Guided Generalist Anomaly Detection [24.763955488489852]
  Generalist Anomaly Detection (GAD) aims to train a unified model on an original domain that can detect anomalies in new target domains.<n>Previous GAD methods primarily use only normal samples as references, overlooking the valuable information contained in anomalous samples that are often available in real-world scenarios.<n>We propose normal-abnormal-guided generalist anomaly detection, which leverages both normal and anomalous samples as references to guide anomaly detection across diverse domains.
  arXiv  Detail & Related papers  (2025-10-01T04:27:10Z)
• Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection [56.57122939745213]
  Anomaly detection focuses on identifying samples that deviate from the norm.<n>Recent self-supervised methods have successfully learned such representations by employing prior knowledge about anomalies to create synthetic outliers during training.<n>We address this limitation with our new approach Con$$, which leverages prior knowledge about symmetries in normal samples to observe the data in different contexts.
  arXiv  Detail & Related papers  (2024-05-29T07:59:06Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly.<n> equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset.<n>Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [25.629973843455495]
  Generalist Anomaly Detection (GAD) aims to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without further training on the target data. We introduce a novel approach that learns an in-context residual learning model for GAD, termed InCTRL. InCTRL is the best performer and significantly outperforms state-of-the-art competing methods.
  arXiv  Detail & Related papers  (2024-03-11T08:07:46Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Open-Set Graph Anomaly Detection via Normal Structure Regularisation [30.638274744518682]
  Open-set Graph Anomaly Detection (GAD) aims to train a detection model using a small number of normal and anomaly nodes.<n>Current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes.<n>We propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to achieve generalised detection ability to unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-12T13:25:28Z)
• Open-Set Multivariate Time-Series Anomaly Detection [7.127829790714167]
  Time-series anomaly detection methods assume that only normal samples are available during the training phase. Supervised methods can be utilized to classify normal and seen anomalies, but they tend to overfit to the seen anomalies during training. We propose the first algorithm to address the open-set TSAD problem, called Multivariate Open-Set Time-Series Anomaly Detector (MOSAD) MOSAD is a novel multi-head TSAD framework with a shared representation space and specialized heads, including the Generative head, the Discriminative head, and the Anomaly-Aware Contrastive head.
  arXiv  Detail & Related papers  (2023-10-18T19:55:11Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)

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.