Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion

• URL: http://arxiv.org/abs/2601.08022v1
• Date: Mon, 12 Jan 2026 21:55:31 GMT
• Title: Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion
• Authors: Samet Hicsonmez, Abd El Rahman Shabayek, Djamila Aouada,
• Abstract summary: Zero-Shot image Anomaly Detection (ZSAD) aims to detect and localise anomalies without access to any normal training samples of the target data.<n>Recent approaches leverage additional modalities such as language to generate fine-grained prompts for localisation.<n>We introduce a training-free vision-only ZSAD framework that circumvents the need for fine-grained prompts.
• Score: 15.486565360380203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-Shot image Anomaly Detection (ZSAD) aims to detect and localise anomalies without access to any normal training samples of the target data. While recent ZSAD approaches leverage additional modalities such as language to generate fine-grained prompts for localisation, vision-only methods remain limited to image-level classification, lacking spatial precision. In this work, we introduce a simple yet effective training-free vision-only ZSAD framework that circumvents the need for fine-grained prompts by leveraging the inversion of a pretrained Denoising Diffusion Implicit Model (DDIM). Specifically, given an input image and a generic text description (e.g., "an image of an [object class]"), we invert the image to obtain latent representations and initiate the denoising process from a fixed intermediate timestep to reconstruct the image. Since the underlying diffusion model is trained solely on normal data, this process yields a normal-looking reconstruction. The discrepancy between the input image and the reconstructed one highlights potential anomalies. Our method achieves state-of-the-art performance on VISA dataset, demonstrating strong localisation capabilities without auxiliary modalities and facilitating a shift away from prompt dependence for zero-shot anomaly detection research. Code is available at https://github.com/giddyyupp/DIVAD.

Related papers

• SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling [6.476948781728136]
  SubspaceAD is a training-free method for detecting visual anomalies in industrial images.<n>It works across one-shot and few-shot settings without training, prompt tuning, or memory banks.<n>It achieves image-level and pixel-level AUROC of 98.0% and 97.6% on the MVTec-AD dataset, and 93.3% and 98.3% on the VisA dataset.
  arXiv  Detail & Related papers  (2026-02-26T13:52:57Z)
• LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling [38.700993166492495]
  We propose a dataset-free, and unified approach through recurrent posterior sampling utilizing a pretrained latent diffusion model.<n>Our method incorporates the multimodal understanding model to provide sematic priors for the generative model under a task-blind condition.
  arXiv  Detail & Related papers  (2025-07-01T14:25:09Z)
• Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection [58.87142367781417]
  A naively trained detector tends to favor overfitting to the limited and monotonous fake patterns, causing the feature space to become highly constrained and low-ranked.<n>One potential remedy is incorporating the pre-trained knowledge within the vision foundation models to expand the feature space.<n>By freezing the principal components and adapting only the remained components, we preserve the pre-trained knowledge while learning fake patterns.
  arXiv  Detail & Related papers  (2024-11-23T19:10:32Z)
• Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme [19.732769780675977]
  Pre-trained vision-language models (VLMs) are highly adaptable to various downstream tasks through few-shot learning. Traditional methods depend on human-crafted prompts that require prior knowledge of specific anomaly types. Our goal is to develop a human-free prompt-based anomaly detection framework that optimally learns prompts through data-driven methods.
  arXiv  Detail & Related papers  (2024-06-26T09:29:05Z)
• DiffUHaul: A Training-Free Method for Object Dragging in Images [78.93531472479202]
  We propose a training-free method, dubbed DiffUHaul, for the object dragging task. We first apply attention masking in each denoising step to make the generation more disentangled across different objects. In the early denoising steps, we interpolate the attention features between source and target images to smoothly fuse new layouts with the original appearance.
  arXiv  Detail & Related papers  (2024-06-03T17:59:53Z)
• MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image Translation by Prompts Redescription and Beyond [57.14128305383768]
  We propose a prompt redescription strategy to realize a mirror effect between the source and reconstructed image in the diffusion model (MirrorDiffusion) MirrorDiffusion achieves superior performance over the state-of-the-art methods on zero-shot image translation benchmarks.
  arXiv  Detail & Related papers  (2024-01-06T14:12:16Z)
• 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)
• PNI : Industrial Anomaly Detection using Position and Neighborhood Information [6.316693022958221]
  We propose a new algorithm, textbfPNI, which estimates the normal distribution using conditional probability given neighborhood features. We conducted experiments on the MVTec AD benchmark dataset and achieved state-of-the-art performance, with textbf99.56% and textbf98.98% AUROC scores in anomaly detection and localization.
  arXiv  Detail & Related papers  (2022-11-22T23:45:27Z)
• EDICT: Exact Diffusion Inversion via Coupled Transformations [13.996171129586731]
  Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem. We propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors.
  arXiv  Detail & Related papers  (2022-11-22T18:02:49Z)
• 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)
• Prefix Conditioning Unifies Language and Label Supervision [84.11127588805138]
  We show that dataset biases negatively affect pre-training by reducing the generalizability of learned representations. In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
  arXiv  Detail & Related papers  (2022-06-02T16:12:26Z)
• 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)
• Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders [81.30960319178725]
  We propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs) First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training.
  arXiv  Detail & Related papers  (2020-06-10T21:28:13Z)

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.