AI 中文总结
提出PARSEE-VAD，一种免训练在线视频异常检测框架，通过命题感知推理提取当前窗口语义，并以流式证据升级维护时间连续性，在四个基准上实现高效在线检测。
AI 中文摘要
免训练的在线视频异常检测（VAD）在使用冻结的多模态语言模型时面临两个相互关联的挑战：在因果和计算约束下提取可靠的当前窗口语义，以及在不重复传输高维历史信息的情况下维持时间连续性。通过文本编码历史可以压缩视觉证据，但会引入语义偏差，而保留视觉历史则会扩大多模态上下文。我们提出PARSEE-VAD，一个双模块框架，将语义证据获取与分数状态演化分离。命题感知推理（PAR）从当前因果窗口中提取结构化的命题证据，并在粗粒度证据需要进一步细化时有条件地激活更具体的查询。通过在查询间共享可复用的因果视觉前缀，PAR通过选择性执行减少了冗余计算。流式证据升级（SEE）通过当前证据升级将获取的命题证据映射为紧凑的分数域事件状态，然后仅传播由此产生的有界状态以支持跨决策的时间连续性。在四个基准上的实验表明，该方法具有强大的免训练在线性能，同时选择性路由减少了专家计算，且分数状态传播保持稀疏。这些结果支持流式多模态推理的当前优先原则：先解析当前语义，再仅使用紧凑的历史状态来修复残余的连续性缺口。
英文摘要
Training-free online video anomaly detection (VAD) with frozen multimodal language models faces two coupled challenges: extracting reliable current-window semantics under causal and computational constraints, and maintaining temporal continuity without repeatedly transmitting high-dimensional history. Encoding history through text can compress visual evidence and introduce semantic bias, whereas retaining visual history expands multimodal context. We introduce PARSEE-VAD, a two-module framework that separates semantic evidence acquisition from score-state evolution. Proposition-Aware Reasoning (PAR) extracts structured propositional evidence from the current causal window and conditionally activates more specific queries when coarse evidence warrants further refinement. By sharing a reusable causal visual prefix across queries, PAR reduces redundant computation through selective execution. Streaming Evidence Escalation (SEE) maps the acquired proposition evidence into a compact score-domain event state through current evidence escalation, then propagates only the resulting bounded state across decisions to support temporal continuity. Experiments on four benchmarks demonstrate strong training-free online performance while selective routing reduces specialist computation and score-state propagation remains sparse. These results support a current-first principle for streaming multimodal inference: resolve present semantics first, then use compact historical state only to repair residual continuity gaps.
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