用视觉增强时间序列异常检测，实现零样本下精准定位。
VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
- 通过视觉-时间细粒度对齐，融合时序与图像模态。
- 零样本下优于现有模型，定位更准且计算开销更低。
- 适合需要高精度异常检测的工业场景应用。
时间序列异常检测（TSAD）需识别即时点异常和长程上下文异常。现有基础模型存在根本矛盾：一维时序模型虽能精确定位点异常，却缺乏全局上下文视角；二维视觉模型可捕捉全局模式，但因缺乏时序对齐导致信息瓶颈，点级检测粗略。为此，我们提出VETime，首个通过细粒度视觉-时序对齐与动态融合统一时序与视觉模态的TSAD框架。VETime引入可逆图像转换与像素级时序对齐模块，建立共享的视觉-时序时间线，既保留判别性细节又保持时序敏感性。此外，设计异常窗口对比学习机制与任务自适应多模态融合，以自适应整合双模态互补感知能力。大量实验表明，VETime在零样本场景下显著优于当前最优模型，实现更高定位精度且计算开销低于现有视觉方法。代码已开源：https://github.com/yyyangcoder/VETime。
原文摘要 · Abstract (English)
Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental trade-off: 1D temporal models provide fine-grained pointwise localization but lack a global contextual perspective, while 2D vision-based models capture global patterns but suffer from information bottlenecks due to a lack of temporal alignment and coarse-grained pointwise detection. To resolve this dilemma, we propose VETime, the first TSAD framework that unifies temporal and visual modalities through fine-grained visual-temporal alignment and dynamic fusion. VETime introduces a Reversible Image Conversion and a Patch-Level Temporal Alignment module to establish a shared visual-temporal timeline, preserving discriminative details while maintaining temporal sensitivity. Furthermore, we design an Anomaly Window Contrastive Learning mechanism and a Task-Adaptive Multi-Modal Fusion to adaptively integrate the complementary perceptual strengths of both modalities. Extensive experiments demonstrate that VETime significantly outperforms state-of-the-art models in zero-shot scenarios, achieving superior localization precision with lower computational overhead than current vision-based approaches. Code available at: https://github.com/yyyangcoder/VETime.
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