# CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities

arXiv: https://arxiv.org/abs/2312.14556 · HTML: https://mlsanity.com/paper/2312.14556/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 87 of its 120 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 30 of 87)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines](https://mlsanity.com/paper/2005.00343/) (2020)
- [Omnivore: A Single Model for Many Visual Modalities](https://mlsanity.com/paper/2201.08377/) (2022)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [Cross-task weakly supervised learning from instructional videos](https://mlsanity.com/paper/1903.08225/) (2019)
- [Audio-Visual Instance Discrimination with Cross-Modal Agreement](https://mlsanity.com/paper/2004.12943/) (2020)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World](https://mlsanity.com/paper/2309.17024/) (2023)
- [Temporal Cycle-Consistency Learning](https://mlsanity.com/paper/1904.07846/) (2019)

## Built on by (most cited first, 30 of 51)

- [HD-EPIC: A Highly-Detailed Egocentric Video Dataset](https://mlsanity.com/paper/2502.04144/) (2025)
- [Proactive Assistant Dialogue Generation from Streaming Egocentric Videos](https://mlsanity.com/paper/2506.05904/) (2025)
- [Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos](https://mlsanity.com/paper/2406.01486/) (2024)
- [OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation](https://mlsanity.com/paper/2509.05513/) (2025)
- [Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks](https://mlsanity.com/paper/2503.22405/) (2025)
- [Vid2Coach: Transforming How-To Videos into Task Assistants](https://mlsanity.com/paper/2506.00717/) (2025)
- [TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos](https://mlsanity.com/paper/2411.02570/) (2024)
- [ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding](https://mlsanity.com/paper/2410.22211/) (2024)
- [EgoMe: A New Dataset and Challenge for Following Me via Egocentric View in Real World](https://mlsanity.com/paper/2501.19061/) (2025)
- [IMPACT: A Dataset for Multi-Granularity Human Procedural Action Understanding in Industrial Assembly](https://mlsanity.com/paper/2604.10409/) (2026)
- [Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation](https://mlsanity.com/paper/2411.13059/) (2024)
- [Procedural Mistake Detection via Action Effect Modeling](https://mlsanity.com/paper/2512.03474/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [EgoOops: A Dataset for Mistake Action Detection from Egocentric Videos referring to Procedural Texts](https://mlsanity.com/paper/2410.05343/) (2024)
- [ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly](https://mlsanity.com/paper/2509.02949/) (2025)
- [Building Egocentric Procedural AI Assistant: Methods, Benchmarks, and Challenges](https://mlsanity.com/paper/2511.13261/) (2025)
- [Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos](https://mlsanity.com/paper/2607.11523/) (2026)
- [Transparent and Coherent Procedural Mistake Detection](https://mlsanity.com/paper/2412.11927/) (2024)
- [PhysLab: A Benchmark Dataset for Multi-Granularity Visual Parsing of Physics Experiments](https://mlsanity.com/paper/2506.06631/) (2025)
- [Can AI Understand the Language of Origami?](https://mlsanity.com/paper/2603.13856/) (2026)
- [OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants](https://mlsanity.com/paper/2605.26485/) (2026)
- [Personal Visual Context Learning in Large Multimodal Models](https://mlsanity.com/paper/2605.10936/) (2026)
- ["Is This It?": Towards Ecologically Valid Benchmarks for Situated Collaboration](https://mlsanity.com/paper/2409.10525/) (2024)
- [Task Graph Maximum Likelihood Estimation for Procedural Activity Understanding in Egocentric Videos](https://mlsanity.com/paper/2502.17753/) (2025)
- [Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?](https://mlsanity.com/paper/2511.21998/) (2025)
- [ENIGMA-360: An Ego-Exo Dataset for Human Behavior Understanding in Industrial Scenarios](https://mlsanity.com/paper/2603.09741/) (2026)
- [MistExit: Learning to Exit for Early Mistake Detection in Procedural Videos](https://mlsanity.com/paper/2603.14252/) (2026)
- [The Unreasonable Effectiveness of VLMs for Zero-shot Procedural Mistake Detection](https://mlsanity.com/paper/2606.21579/) (2026)
- [EgoProceVQA: A Novel Egocentric Procedural Understanding Task with Self-Skill-Exploration Agent](https://mlsanity.com/paper/2607.13792/) (2026)
- [I2G: Generating Instructional Illustrations via Text-Conditioned Diffusion](https://mlsanity.com/paper/2505.16425/) (2025)

## Abstract (arXiv)

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