Real-world industrial scenarios have become increasingly dynamic, with new product types, defect patterns, and operational modes emerging rapidly. In such a context, the one-for-more paradigm enables the use of a single model to economically and continually adapt to evolving distributions or patterns, positioning it as a key component in modern Industrial AI systems. This article proposes a novel one-for-more anomaly detection framework designed to identify anomalies across expanding product lines. The framework incorporates two model-agnostic techniques: instance-aware prompt tuning (IPT) and gradient-aware parameter decoupling (GPD). Our approach is built upon a reconstruction-based vision transformer (ViT) encoder–decoder architecture. IPT addresses the domain gap between pretrained models and industrial data by leveraging an instance-level prompt and a shared memory mechanism, which helps the pretrained model retain previously learned patterns. GPD selectively updates network parameters based on the gradient’s impact on prior tasks, employing orthogonal gradient projection to further minimize interference. In addition, we introduce a new dataset to simulate the one-for-more industrial scenario. Extensive experiments on MVTec and our proposed dataset demonstrate that our framework achieves the state-of-the-art performance across various continual learning settings, significantly outperforming existing methods, particularly in multistep incremental scenarios.

Industrial few-shot anomaly detection (FSAD) requires identifying various abnormal states by leveraging as few normal samples as possible (abnormal samples are unavailable during training). However, current methods often require training a separate model for each category, leading to increased computation and storage overhead. Thus, designing a unified anomaly detection model that supports multiple categories remains a challenging task, as such a model must recognize anomalous patterns across diverse objects and domains. To tackle these challenges, this paper introduces FocusPatch AD, a unified anomaly detection framework based on vision-language models, achieving anomaly detection under few-shot multi-class settings. FocusPatch AD links anomaly state keywords to highly relevant discrete local regions within the image, guiding the model to focus on cross-category anomalies while filtering out background interference. This approach mitigates the false detection issues caused by global semantic alignment in vision-language models. We evaluate the proposed method on the MVTec, VisA, and Real-IAD datasets, comparing them against several prevailing anomaly detection methods. In both image-level and pixel-level anomaly detection tasks, FocusPatch AD achieves significant gains in classification and localization performance, demonstrating excellent generalization and adaptability.

With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. However, our study found that the diffusion model suffers from severe "faithfulness hallucination" and "catastrophic forgetting", which can’t meet the unpredictable pattern increments. To mitigate the above problems, we propose a continual diffusion model that uses gradient projection to achieve stable continual learning. Gradient projection deploys a regularization on the model updating by modifying the gradient towards the direction protecting the learned knowledge. But as a double-edged sword, it also requires huge memory costs brought by the Markov process. Hence, we propose an iterative singular value decomposition method based on the transitive property of linear representation, which consumes tiny memory and incurs almost no performance loss. Finally, considering the risk of "over-fitting" to normal images of the diffusion model, we propose an anomaly-masked network to enhance the condition mechanism of the diffusion model. For continual anomaly detection, ours achieves first place in 17/18 settings on MVTec and VisA. Code is available at https://github.com/FuNz-0/One-for-More

The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering. For automated scenarios, we first use conventional prompt learning with many-class paradigm as the baseline to automatically learn prompts but found that it can not work well in one-class anomaly detection. To address the above problem, this paper proposes a one-class prompt learning method for few-shot anomaly detection, termed PromptAD. First, we propose semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes, thus constructing a large number of negative samples used to guide prompt learning in one-class setting. Furthermore, to mitigate the training challenge caused by the absence of anomaly images, we introduce the concept of explicit anomaly margin, which is used to explicitly control the margin between normal prompt features and anomaly prompt features through a hyper-parameter. For image-level/pixel-level anomaly detection, PromptAD achieves first place in 11/12 few-shot settings on MVTec and VisA. Code is available at https://github.com/FuNz-0/PromptAD.git

Zero-shot learning image classification aims to identify unseen classes not present during training. Generalized zero-shot learning (GZSL) is more in line with realistic scenarios due to its ability of recognizing both seen and unseen classes. Current GZSL methods mostly utilize generative adversarial networks (GANs) but typically follow a two-stage training: first, training the GAN and then, using its synthetic features to train a classifier, which is limited by isolated optimizations rather than federated. We propose a novel One-stage Training Generative Paradigm that incorporates the classifier as a unique synthetic label generator and builds a three-player game involving a generator, discriminator, and classifier, which ensures a unified optimization objective, eliminating the discrete optimization approach of two-stage methods. We also propose a label-attribute classifier that leverages both labels and attributes, surpassing traditional softmax classifiers that only use labels. Our test results show the effectiveness of the proposed methods.

Unlike conventional zero-shot learning (CZSL) which only focuses on the recognition of unseen classes by using the classifier trained on seen classes and semantic embeddings, generalized zero-shot learning (GZSL) aims at recognizing both the seen and unseen classes, so it is more challenging due to the extreme training imbalance. Recently, some feature generation methods introduce metric learning to enhance the discriminability of visual features. Although these methods achieve good results, they focus only on metric learning in the visual feature space to enhance features and ignore the association between the feature space and the semantic space. Since the GZSL method uses semantics as prior knowledge to migrate visual knowledge to unseen classes, the consistency between visual space and semantic space is critical. To this end, we propose relational metric learning which can relate the metrics in the two spaces and make the distribution of the two spaces more consistent. Based on the generation method and relational metric learning, we proposed a novel GZSL method, termed VS-Boost, which can effectively boost the association between vision and semantics. The experimental results demonstrate that our method is effective and achieves significant gains on five benchmark datasets compared with the state-of-the-art methods.

Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can recognize objects from both seen and unseen classes. Due to the absence of unseen training samples, the classifier tends to bias towards seen classes. To mitigate this problem, feature generation based models are proposed to synthesize visual features for unseen classes. However, these features are generated in the visual feature space which lacks of discriminative ability. Therefore, some methods turn to find a better embedding space for the classifier training. They emphasize the inter-class relationships of seen classes, leading the embedding space overfitted to seen classes and unfriendly to unseen classes. Instead, in this paper, we propose an Intra-Class Compactness Enhancement method (ICCE) for GZSL. Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space. By promoting the intra-class relationships but the inter-class structures, we can distinguish different classes with better generalization. Specifically, we propose a Self-Distillation Embedding (SDE) module and a Semantic-Visual Contrastive Generation (SVCG) module. The former promotes intra-class compactness in the embedding space, while the latter accomplishes it in the visual feature space. The experiments demonstrate that our ICCE outperforms the state-of-the-art methods on four datasets and achieves competitive results on the remaining dataset.