When Anomalies Are Too Small to See: Organizing evidence for Video Anomaly Detection

Video anomaly detection (VAD) aims to identify events that deviate from normal activity patterns specific to a scene. Given the open-set and scene-dependent nature of anomalies, recent work leverages semantic priors from pretrained vision-language models (VLMs) for detection without target-scene training. These methods have achieved strong performance in frame-level anomaly detection. However, they struggle with small-object anomalies in complex scenes, primarily because subtle anomaly cues from small objects are easily overwhelmed by the dominant normal background. This subproblem remains largely underexplored. To address this gap, we present SAVOR, a framework for detecting small-object anomalies from three complementary perspectives: temporal modeling, spatial refinement, and cross-frame association. Specifically, we propose Temporal Compression Mosaic (TCM) to adaptively select keyframes and highlight motion-salient objects. Then, Spatial Event Reasoning (SER) consolidates spatially sparse trajectory observations into persistent object events, enabling joint reasoning over complementary global and local anomaly evidence. Finally, to mitigate tracking errors, Anchor Prompt Retargeting (APR) realigns unreliable anchor prompts via mask feedback to guide bidirectional SAM2 propagation, preserving object identity and mask stability. The pipeline requires no in-domain training or parameter updates. Comprehensive experiments on ShanghaiTech and UBnormal demonstrate that our method outperforms existing zero-shot and training-free approaches at both pixel and object levels. Additional evaluations on SOAL, a newly constructed small-object anomaly benchmark curated from standard VAD datasets, confirm substantial improvements in spatial localization and temporal tracking.