Following step-by-step procedures is an essential component of various
activities carried out by individuals in their daily lives. These procedures
serve as a guiding framework that helps to achieve goals efficiently, whether
it is assembling furniture or preparing a recipe. However, the complexity and
duration of procedural activities inherently increase the likelihood of making
errors. Understanding such procedural activities from a sequence of frames is a
challenging task that demands an accurate interpretation of visual information
and the ability to reason about the structure of the activity. To this end, we
collect a new egocentric 4D dataset, CaptainCook4D, comprising 384 recordings
(94.5 hours) of people performing recipes in real kitchen environments. This
dataset consists of two distinct types of activity: one in which participants
adhere to the provided recipe instructions and another in which they deviate
and induce errors. We provide 5.3K step annotations and 10K fine-grained action
annotations and benchmark the dataset for the following tasks: supervised error
recognition, multistep localization, and procedure learning

我们收集了一个全新的心理 4D 数据集 CaptainCook4D，包含了 384 个人在真实厨房环境中表演食谱的记录（共 94.5 小时），该数据集包含两种不同类型的活动：一种是参与者按照提供的食谱指示进行，另一种是参与者偏离指示并引发错误，我们提供了 5.3K 个步骤注释和 10K 个精细动作注释，并对该数据集进行了监督错误识别、多步本地化和程序学习的基准测试。

CaptainCook4D：过程活动错误理解数据集

CaptainCook4D: A dataset for understanding errors in procedural  activities

Brain-Computer Interfaces (BCI) have allowed for direct communication from
the brain to external applications for the automatic detection of cognitive
processes such as error recognition. Error-related potentials (ErrPs) are a
particular brain signal elicited when one commits or observes an erroneous
event. However, due to the noisy properties of the brain and recording devices,
ErrPs vary from instance to instance as they are combined with an assortment of
other brain signals, biological noise, and external noise, making the
classification of ErrPs a non-trivial problem. Recent works have revealed
particular cognitive processes such as awareness, embodiment, and
predictability that contribute to ErrP variations. In this paper, we explore
the performance of classifier transferability when trained on different ErrP
variation datasets generated by varying the levels of awareness and embodiment
for a given task. In particular, we look at transference between observational
and interactive ErrP categories when elicited by similar and differing tasks.
Our empirical results provide an exploratory analysis into the ErrP
transferability problem from a data perspective.

研究脑 - 计算机接口中一个重要的问题：如何分类错误相关的大脑信号。本文探索分类器可迁移性的表现，研究了不同认知因素对错误相关电位变化的影响，并通过数据分析提供了一种解决方案。