We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive research exists, most efforts either focus on specific tasks, i.e., anomalies or normal products only, or require separate models for each anomaly type. Consequently, prior methods either offer limited generative capability or depend on a vast array of anomaly-specific models. We demonstrate that U-Net's differentiated learning ability captures the distinct visual traits of slightly-varied normal products and diverse anomalies, enabling us to construct a unified model for all tasks. Specifically, we first introduce an Unbalanced Abnormal (UA) Text Prompt, comprising one normal token and multiple anomaly tokens. More importantly, our Decoupled Anomaly Alignment (DA) loss decouples anomaly attributes and binds them to distinct anomaly tokens of UA, enabling SeaS to create unseen anomalies by recombining these attributes. Furthermore, our Normal-image Alignment (NA) loss aligns the normal token to normal patterns, making generated normal products globally consistent and locally varied. Finally, SeaS produces accurate anomaly masks by fusing discriminative U-Net features with high-resolution VAE features. SeaS sets a new benchmark for industrial generation, significantly enhancing downstream applications, with average improvements of $+8.66\%$ pixel-level AP for synthesis-based AD approaches, $+1.10\%$ image-level AP for unsupervised AD methods, and $+12.79\%$ IoU for supervised segmentation models. Code is available at \href{https://github.com/HUST-SLOW/SeaS}{https://github.com/HUST-SLOW/SeaS}.

Video camera plays a growing important role in advancing industrial control towards a higher level of automation. Thus, video analytics become highly demanded, especially for low-latency and high-accuracy analytic results. Yet, the data volume produced by camera clusters is prohibitively high. In this article, we proposeRespire, a system that can remove redundant frames for reducing the cost of transmission and processing based on edge computing nodes, while maintaining useful frames for high analytic accuracy. Specifically,Respireincorporates a new way for characterizing the spatial–temporal redundancy between frames. Then,Respireprioritizes the uploading of frames for redundancy reduction. As the search space of the entire collected frames is exponential for the set of frames containing the maximal information, we jointly consider offline and online pruning of frames and propose a heuristic algorithm to reduce the search space. Extensive real-world dataset-based experiments demonstrate that the proposed system can significantly reduce communication and computation costs, while providing sufficient information for guaranteed video analytic accuracy.