用超维计算优化大模型生成的缺陷推理图，提升视频异常检测效果
MissionHD: Hyperdimensional Refinement of Distribution-Deficient Reasoning Graphs for Video Anomaly Detection
- 在超维空间中直接优化大模型生成的推理图结构
- 在弱监督视频异常检测任务中实现稳定性能提升
- 适合需要高效构建可解释推理路径的视频理解场景
大模型生成的任务特定推理图（MSGs）在视频异常检测（VAD）和识别（VAR）中日益重要，但通常被视为固定结构，存在分布不足问题。传统图结构精炼方法依赖结构分布学习，不适用于此类通用且分布缺失的图。本文提出HDC约束的图结构精炼（HDC-GSR），在单一超维空间中直接优化可解码、任务对齐的图表示，无需建模分布。利用超维计算（HDC），框架通过绑定与捆绑操作编码图，将图代码与下游损失对齐，并解码边贡献以精炼结构。我们将其实例化为MissionHD，用于弱监督的VAD/VAR，在基准数据集上展现持续性能提升。
原文摘要 · Abstract (English)
LLM-generated reasoning graphs, referred to as mission-specific graphs (MSGs), are increasingly used for video anomaly detection (VAD) and recognition (VAR). However, they are typically treated as fixed despite being generic and distribution-deficient. Conventional graph structure refinement (GSR) methods are ill-suited to this setting, as they rely on learning structural distributions that are absent in LLM-generated graphs. We propose HDC-constrained Graph Structure Refinement (HDC-GSR), a new paradigm that directly optimizes a decodable, task-aligned graph representation in a single hyperdimensional space without distribution modeling. Leveraging Hyperdimensional Computing (HDC), our framework encodes graphs via binding and bundling operations, aligns the resulting graph code with downstream loss, and decodes edge contributions to refine the structure. We instantiate this approach as MissionHD for weakly supervised VAD/VAR and demonstrate consistent performance gains on benchmark datasets.
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