建模多种正常动作表示，提升流程任务中错误检测的准确性
Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks
- 预测多个可能的下一步动作并重建其正常表示
- 在真实场景下误差检测准确率超越现有方法
- 适合需要高可靠性的机器人与AR辅助系统
流程任务中的错误检测对增强现实辅助和机器人系统的一致性与正确性至关重要。现有方法多关注时间顺序错误或依赖静态原型表示正常动作，但往往忽视了给定动作序列后存在多种合法后续动作的常见情况。这导致两个问题：(1) 当推理环境或动作分布与训练阶段不一致时，静态原型难以有效检测错误；(2) 若当前动作标签与预测不一致，模型可能使用错误的原型进行检测。为此，我们提出自适应多正常动作表示（AMNAR）框架。AMNAR预测所有可能的下一动作，并重构其对应的正常动作表示，再与实际执行动作对比以检测错误。大量实验表明，AMNAR达到当前最优性能，验证了建模多种合法后续动作在错误检测中的重要性。代码已开源：https://github.com/iSEE-Laboratory/AMNAR。
原文摘要 · Abstract (English)
Error detection in procedural activities is essential for consistent and correct outcomes in AR-assisted and robotic systems. Existing methods often focus on temporal ordering errors or rely on static prototypes to represent normal actions. However, these approaches typically overlook the common scenario where multiple, distinct actions are valid following a given sequence of executed actions. This leads to two issues: (1) the model cannot effectively detect errors using static prototypes when the inference environment or action execution distribution differs from training; and (2) the model may also use the wrong prototypes to detect errors if the ongoing action label is not the same as the predicted one. To address this problem, we propose an Adaptive Multiple Normal Action Representation (AMNAR) framework. AMNAR predicts all valid next actions and reconstructs their corresponding normal action representations, which are compared against the ongoing action to detect errors. Extensive experiments demonstrate that AMNAR achieves state-of-the-art performance, highlighting the effectiveness of AMNAR and the importance of modeling multiple valid next actions in error detection. The code is available at https://github.com/iSEE-Laboratory/AMNAR.
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