发表机构
University of Maryland（马里兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
VidParse是一种无需训练的在线框架，通过图约束推理处理第一人称视角视频的流程解析，在复杂多步骤解析精度上较基线提升最高达10倍。
AI 中文摘要
将连续、含噪声的第一人称视角视频流转换为离散、时间有序的动作步骤存在诸多视觉挑战。强烈的自我运动、短暂遮挡以及非剧本化人-物交互的高类内变异性，导致标准帧级在线时间模型难以应对，常出现严重的过分割和结构崩溃问题。为弥合不稳定的低级感知与高级流程逻辑之间的差距，我们提出VidParse，这是一种无需训练的在线框架，将活动理解视为图约束推理问题。该框架不依赖学习到的时间滤波器，而是通过基于操作锚定特征的时间相似度矩阵动态识别语义转换，这些特征从冻结的基础模型中提取，以优先考虑前景手-物交互。随后，束搜索解码器利用诱导的流程任务图，明确强制有效的动作转换并修剪不可能的轨迹。通过将鲁棒视觉片段锚定到严格的流程约束，我们的方法保留了远程状态转换，在复杂多步骤解析精度上比强大的在线基线实现了高达10倍的提升，且无需进行任何梯度更新。
英文摘要
Translating continuous, noisy egocentric video streams into discrete, temporally ordered action steps is fraught with visual challenges. Heavy ego-motion, transient occlusions, and the high intra-class variability of unscripted human-object interactions cause standard frame-level online temporal models to struggle, often resulting in severe over-segmentation and structural collapse. To bridge the gap between unstable low-level perception and high-level procedural logic, we present VidParse, an online, training-free framework that treats activity understanding as a graph-constrained inference problem. Rather than relying on learned temporal filters, we dynamically identify semantic transitions using a temporal similarity matrix over manipulation-anchored features, which are extracted from frozen foundation models to prioritize foreground hand-object interactions. A beam search decoder then leverages an induced procedural task graph to explicitly enforce valid action transitions and prune impossible trajectories. By anchoring robust visual segments to hard procedural constraints, our approach preserves long-range state transitions and achieves up to a 10x improvement in complex multi-step parsing accuracy over strong online baselines, all without requiring a single gradient update.
CommentsAccepted at ECCV 2026
