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.

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

Prompt-tuning has demonstrated impressive performance in continual learning by querying relevant prompts for each input instance, which can avoid the introduction of task identifier. Its forgetting is therefore reduced as this instance-wise query mechanism enables us to select and update only relevant prompts. In this paper, we further integrate prompt-tuning with gradient projection approach. Our observation is: prompt-tuning releases the necessity of task identifier for gradient projection method; and gradient projection provides theoretical guarantees against forgetting for prompt-tuning. This inspires a new prompt gradient projection approach (PGP) for continual learning. In PGP, we deduce that reaching the orthogonal condition for prompt gradient can effectively prevent forgetting via the self-attention mechanism in vision-transformer. The condition equations are then realized by conducting Singular Value Decomposition (SVD) on an element-wise sum space between input space and prompt space. We validate our method on diverse datasets and experiments demonstrate the efficiency of reducing forgetting both in class incremental, online class incremental, and task incremental settings. The code is available at https://github.com/JingyangQiao/prompt-gradient-projection.

Brain tumor anomaly detection plays a critical role in the field of computer-aided diagnosis, which has attracted ever-increasing focus from the medical community However, brain tumor data are scarce and tough to classify. Unsupervised methods enable the reduction of huge labeling costs to be applied to brain tumor anomaly detection during the training only given normal brain images. However, the existing unsupervised methods distinguish whether the input image is abnormal in the image space, which cannot effectively learn the discriminative features. In this paper, we propose a novel brain tumor anomaly detection method via Latent Feature Regularization based Adversarial Network (LFRA-Net), which leverages a latent feature regularizer into adversarial learning to obtain the discriminative features. Comprehensive experiments on BraTS, HCP, MNIST, and CIFAR-10 datasets evaluate the effectiveness of our LFRANet, which outperforms state-of-the-art unsupervised learning methods.

Video anomaly detection aims to automatically identify unusual objects or behaviours by learning from normal videos. Previous methods tend to use simplistic reconstruction or prediction constraints, which leads to the insufficiency of learned representations for normal data. As such, we propose a novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. First, predictive consistency is proposed to consider the symmetry property of motion and appearance in forwards and backwards time, which ensures the highly realistic appearance and motion predictions at the pixel-wise level. Second, association consistency considers the relevance between different modalities and uses one modality to regularize the prediction of another one. Finally, temporal consistency utilizes the relationship of the video sequence and ensures that the predictive network generates temporally consistent frames. During inference, the pattern of abnormal frames is unpredictable and will therefore cause higher prediction errors. Experiments show that our method outperforms advanced anomaly detectors and achieves state-of-the-art results on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.

Face spoofing causes severe security threats in face recognition systems. The previous anti-spoofing mainly focused on supervised techniques, typically with either binary or auxiliary supervision. Most of them have to ‘see’ both spoofing face data and live face data during training to realize the task of face anti-spoofing. In this paper, we propose a semi-supervised adversarial learning framework for spoof face detection, which largely relaxes the supervision condition. To capture the underlying structure of live face data in latent representation space, we propose to train the live face data only, with a convolutional Encoder-Decoder network acting as a Generator, and a second convolutional network serving as a Discriminator. The generator and discriminator are trained by competing with each other while collaborating to understand the live faces. Since the spoof face detection is video-based (i.e., temporal information), we intuitively take the optical flow maps converted from consecutive video frames as input. Our approach is free of the spoof faces, thus being robust and general to different types of face spoofing (even unknown spoofing). Experiments on cross-dataset tests show that our semi-supervised method achieves better or comparable results to state-of-the-art supervised techniques. We also conduct ablation studies for the proposed method.

Novelty detection is the task of identifying whether a new data point is considered to be an inlier or an outlier. Generative Adversarial Networks (GAN)-based methods suffer from mode dropping and unstable training issue, which poses the greatest threat to learn the target class distribution. To solve mode dropping issues, the nearest neighbor generator is designed to ensure that for every training image there exists a candidate generated image that is near to it at optimality. The generator considers the entire distribution of training data without mode dropping. To avoid the instability training issue, we consider capturing the distribution of the target class by non-adversarial strategy. In addition, to provide great image priors and fully diversity candidate samples for the generator, we also design a two-step mapping process. Finally, Experiments show that our model has clear superiority over cutting-edge novelty detectors and achieves state-of-the-art results on the datasets.

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.

Recent researches have shown great progress in abnormal detection with the application of deep neural network. However, those works tend to solve the task concentrating on homogeneous features or with a decoupled model that combines features inefficiently. In this paper, we propose a method for abnormal detection that learns different features' distributions in low-dimensionalities and combines them in an efficient way. The main architecture of our work consists of a two-stream AutoEncoder and LSTM architecture model to get the compressed low-dimensional spatial and temporal features respectively. Instead of standard Expectation-Maximization algorithm, we further design two estimation network to estimate probability densities and combine them with the Products of Experts. In addition, the experiments of our method on different dataset deliver on-par or superior performance compared to state-of-the-art methods in one-class and abnormal detection settings.

With the development of adversarial attack in deep learning, it is critical for abnormal detector to not only discover the out-of-distribution samples but also provide defence against the adversarial attacker. Since few previous universal detector is known to work well on both tasks, we consider against both scenarios by constructing a robust and effective technique, where one sample could be regarded as the abnormal sample if it exhibits a higher image reconstruction error. Due to the training instability issues existed in previous generative adversarial networks (GANs) based methods, in this paper we propose a dual auxiliary autoencoder to make a tradeoff between the capability of generator and discriminator, leading to a more stable training process and high-quality image reconstruction. Moreover, to generate discriminative and robust latent representations, the mutual information estimator regarded as latent regularizer is adopted to extract the most unique information of target class. Overall, our generative dual adversarial network simultaneously optimizes the image reconstruction space and latent space to improve the performance. Experiments show that our model has the clear superiority over cutting edge semi-supervised abnormal detectors and achieves the state-of-the-art results on the datasets.