Unsupervised visual anomaly detection from multi-view images presents a significant challenge: distinguishing genuine defects from benign appearance variations caused by viewpoint changes. Existing methods, often designed for single-view inputs, treat multiple views as a disconnected set of images, leading to inconsistent feature representations and a high false-positive rate. To address this, we introduce ViewSense-AD (VSAD), a novel framework that learns viewpoint-invariant representations by explicitly modeling geometric consistency across views. At its core is our Multi-View Alignment Module (MVAM), which leverages homography to project and align corresponding feature regions between neighboring views. We integrate MVAM into a View-Align Latent Diffusion Model (VALDM), enabling progressive and multi-stage alignment during the denoising process. This allows the model to build a coherent and holistic understanding of the object's surface from coarse to fine scales. Furthermore, a lightweight Fusion Refiner Module (FRM) enhances the global consistency of the aligned features, suppressing noise and improving discriminative power. Anomaly detection is performed by comparing multi-level features from the diffusion model against a learned memory bank of normal prototypes. Extensive experiments on the challenging RealIAD and MANTA datasets demonstrate that VSAD sets a new state-of-the-art, significantly outperforming existing methods in pixel, view, and sample-level visual anomaly detection, proving its robustness to large viewpoint shifts and complex textures.

Humans detect real-world object anomalies by perceiving, interacting, and reasoning based on object-conditioned physical knowledge. The long-term goal of Industrial Anomaly Detection (IAD) is to enable machines to autonomously replicate this skill. However, current IAD algorithms are largely developed and tested on static, semantically simple datasets, which diverge from real-world scenarios where physical understanding and reasoning are essential. To bridge this gap, we introduce the Physics Anomaly Detection (Phys-AD) dataset, the first large-scale, real-world, physics-grounded video dataset for industrial anomaly detection. Collected using a real robot arm and motor, Phys-AD provides a diverse set of dynamic, semantically rich scenarios. The dataset includes more than 6400 videos across 22 real-world object categories, interacting with robot arms and motors, and exhibits 47 types of anomalies. Anomaly detection in Phys-AD requires visual reasoning, combining both physical knowledge and video content to determine object abnormality. We benchmark state-of-the-art anomaly detection methods under three settings: unsupervised AD, weakly-supervised AD, and video-understanding AD, highlighting their limitations in handling physics-grounded anomalies. Additionally, we introduce the Physics Anomaly Explanation (PAEval) metric, designed to assess the ability of visual-language foundation models to not only detect anomalies but also provide accurate explanations for their underlying physical causes. Our project is available at https://guyao2023.github.io/Phys-AD/.

3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating in-situ anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of 80.2% and 90.0%, respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. The code is available at https://github.com/ZZZBBBZZZ/PASDF to support further research.

Federated learning (FL) represents a distributed machine learning approach that leverages a centralized server to train models while keeping the data on edge devices isolated. FL has the benefits of preserving data privacy and improving model accuracy. However, the occurrence of unexpected device exits during model training can severely impact the performance of the models. To address the communication overhead issue and accelerate model convergence, a novel adaptive FL with a negative inner product aggregation approach, namely, NIPAFed is proposed in this article. The NIPAFed leverages a congestion control algorithm inspired by TCP, known as additive multiplication subtraction strategy, to adaptively predict the workload of devices based on historical workload. So NIPAFed effectively mitigates the impact of stragglers on the training process. Additionally, to reduce communication overhead and latency, a negative inner product aggregation strategy is employed to accelerate model convergence and minimize the number of communication rounds required. The convergence of the model is also analyzed theoretically. The validity of NIPAFed is tested on federated public data sets and the NIPAFed is compared with some algorithms. The experimental results clearly demonstrate the superiority of the NIPAFed in terms of performance. By reducing device dropouts and minimizing the communication rounds, the NIPAFed effectively controls the communication overhead while the convergence is ensured.

A domain adaptation problem is dealt with where the marginal probability in a target domain is different from but correlated to the one in the source domain but the classification tasks are the same. This problem occurs frequently in classification of remote sensing data, e.g., when data are collected in the same area but at different dates or when data are acquired by the same sensor with the same class label set but in different locations. Traditional learning machines cannot deal with this problem in a satisfactory manner. In this paper, we propose a rationale input-output-consistency where samples in the same cluster and defined by spectral signatures (input space) should have the same class label (output space) if they are accurately classified. With the rationale, samples of high confidence in the target domain are selected to define a new prediction function. Since two domains that are related can have different distributions, the data in the source domain which cannot adapt to the distribution in the target domain are deleted from the training data set. Therefore, the proposed algorithm is denoted as input-consistent-output domain adaptation (iCODA) and works in an iterative way. After the selection of highly-confident target samples and the deletion of source data, a new training data set is used to define a new prediction model. The proposed iCODA algorithm was evaluated on EO-1 hyperspectral data sets from Botswana. Experimental results demonstrate much better classification accuracies when compared to a traditionally used supervised classifier.