Ximiao Zhang

Min Xu, Ximiao Zhang, Xiuzhuang Zhou

In this paper, we investigate the problem of prediction confidence in face and kinship verification. Most existing face and kinship verification methods focus on accuracy performance while ignoring confidence estimation for their prediction results. However, confidence estimation is essential for modeling reliability and trustworthiness in such high-risk tasks. To address this, we introduce an effective confidence measure that allows verification models to convert a similarity score into a confidence score for any given face pair. We further propose a confidence-calibrated approach, termed Angular Scaling Calibration (ASC). ASC is easy to implement and can be readily applied to existing verification models without model modifications, yielding accuracy-preserving and confidence-calibrated probabilistic verification models. In addition, we introduce the uncertainty in the calibrated confidence to boost the reliability and trustworthiness of the verification models in the presence of noisy data. To the best of our knowledge, our work presents the first comprehensive confidence-calibrated solution for modern face and kinship verification tasks. We conduct extensive experiments on four widely used face and kinship verification datasets, and the results demonstrate the effectiveness of our proposed approach. Code and models are available at https://github.com/cnulab/ASC.

Ximiao Zhang, Min Xu, Xiuzhuang Zhou

Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization. Despite this progress, these methods still face challenges in synthesizing realistic and diverse anomaly samples, as well as addressing the feature redundancy and pre-training bias of pre-trained feature. In this work, we introduce RealNet, a feature reconstruction network with realistic synthetic anomaly and adaptive feature selection. It is incorporated with three key innovations: First, we propose Strength-controllable Diffusion Anomaly Synthesis (SDAS), a diffusion process-based synthesis strategy capable of generating samples with varying anomaly strengths that mimic the distribution of real anomalous samples. Second, we develop Anomaly-aware Features Selection (AFS), a method for selecting representative and discriminative pre-trained feature subsets to improve anomaly detection performance while controlling computational costs. Third, we introduce Reconstruction Residuals Selection (RRS), a strategy that adaptively selects discriminative residuals for comprehensive identification of anomalous regions across multiple levels of granularity. We assess RealNet on four benchmark datasets, and our results demonstrate significant improvements in both Image AUROC and Pixel AUROC compared to the current state-o-the-art methods. The code, data, and models are available at https://github.com/cnulab/RealNet.

Ximiao Zhang, Min Xu, Zheng Zhang et al.

In this paper, we introduce the task of unified anomaly detection and classification, which aims to simultaneously detect anomalous regions in images and identify their specific categories. Existing methods typically treat anomaly detection and classification as separate tasks, thereby neglecting their inherent correlation, limiting information sharing, and resulting in suboptimal performance. To address this, we propose UniADC, a unified anomaly detection and classification model that can effectively perform both tasks with only a few or even no anomaly images. Specifically, UniADC consists of two key components: a training-free controllable inpainting network and a multi-task discriminator. The inpainting network can synthesize anomaly images of specific categories by repainting normal regions guided by anomaly priors, and can also repaint few-shot anomaly samples to augment the available anomaly data. The multi-task discriminator is then trained on these synthesized samples, enabling precise anomaly detection and classification by aligning fine-grained image features with anomaly-category embeddings. We conduct extensive experiments on three anomaly detection and classification datasets, including MVTec-FS, MTD, and WFDD, and the results demonstrate that UniADC consistently outperforms existing methods in anomaly detection, localization, and classification. The code is available at https://github.com/cnulab/UniADC.

Ximiao Zhang, Min Xu, Dehui Qiu et al.

In the field of medical decision-making, precise anomaly detection in medical imaging plays a pivotal role in aiding clinicians. However, previous work is reliant on large-scale datasets for training anomaly detection models, which increases the development cost. This paper first focuses on the task of medical image anomaly detection in the few-shot setting, which is critically significant for the medical field where data collection and annotation are both very expensive. We propose an innovative approach, MediCLIP, which adapts the CLIP model to few-shot medical image anomaly detection through self-supervised fine-tuning. Although CLIP, as a vision-language model, demonstrates outstanding zero-/fewshot performance on various downstream tasks, it still falls short in the anomaly detection of medical images. To address this, we design a series of medical image anomaly synthesis tasks to simulate common disease patterns in medical imaging, transferring the powerful generalization capabilities of CLIP to the task of medical image anomaly detection. When only few-shot normal medical images are provided, MediCLIP achieves state-of-the-art performance in anomaly detection and location compared to other methods. Extensive experiments on three distinct medical anomaly detection tasks have demonstrated the superiority of our approach. The code is available at https://github.com/cnulab/MediCLIP.

Haoqi Ni, Ximiao Zhang, Min Xu et al.

Chest X-Ray (CXR) examination is a common method for assessing thoracic diseases in clinical applications. While recent advances in deep learning have enhanced the significance of visual analysis for CXR anomaly detection, current methods often miss key cues in anomaly images crucial for identifying disease regions, as they predominantly rely on unsupervised training with normal images. This letter focuses on a more practical setup in which few-shot anomaly images with only image-level labels are available during training. For this purpose, we propose WSCXR, a weakly supervised anomaly detection framework for CXR. WSCXR firstly constructs sets of normal and anomaly image features respectively. It then refines the anomaly image features by eliminating normal region features through anomaly feature mining, thus fully leveraging the scarce yet crucial features of diseased areas. Additionally, WSCXR employs a linear mixing strategy to augment the anomaly features, facilitating the training of anomaly detector with few-shot anomaly images. Experiments on two CXR datasets demonstrate the effectiveness of our approach.

Ximiao Zhang, Min Xu, Xiuzhuang Zhou

Current anomaly detection methods primarily focus on low-resolution scenarios. For high-resolution images, conventional downsampling often results in missed detections of subtle anomalous regions due to the loss of fine-grained discriminative information. Despite some progress, recent studies have attempted to improve detection resolution by employing lightweight networks or using simple image tiling and ensemble methods. However, these approaches still struggle to meet the practical demands of industrial scenarios in terms of detection accuracy and efficiency. To address the above issues, we propose HiAD, a general framework for high-resolution anomaly detection. HiAD is capable of detecting anomalous regions of varying sizes in high-resolution images under limited computational resources. Specifically, HiAD employs a dual-branch architecture that integrates anomaly cues across different scales to comprehensively capture both subtle and large-scale anomalies. Furthermore, it incorporates a multi-resolution feature fusion strategy to tackle the challenges posed by fine-grained texture variations in high-resolution images. To enhance both adaptability and efficiency, HiAD utilizes a detector pool in conjunction with various detector assignment strategies, enabling detectors to be adaptively assigned based on patch features, ensuring detection performance while effectively controlling computational costs. We conduct extensive experiments on our specifically constructed high-resolution anomaly detection benchmarks, including MVTec-HD, VisA-HD, and the real-world benchmark RealIAD-HD, demonstrating the superior performance of HiAD. The code is available at https://github.com/cnulab/HiAD.