# What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

arXiv: https://arxiv.org/abs/1503.01558 · HTML: https://mlsanity.com/paper/1503.01558/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2015.

## Built on by (most cited first, 30 of 31)

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- [STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos](https://mlsanity.com/paper/2301.00794/) (2023)
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- [Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos](https://mlsanity.com/paper/2110.05697/) (2021)
- [OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation](https://mlsanity.com/paper/2309.06276/) (2023)
- [Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actions](https://mlsanity.com/paper/2106.03158/) (2021)
- [Estimating 3D Motion and Forces of Human-Object Interactions from Internet Videos](https://mlsanity.com/paper/2111.01591/) (2021)
- [Rethinking Cooking State Recognition with Vision Transformers](https://mlsanity.com/paper/2212.08586/) (2022)
- [CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation](https://mlsanity.com/paper/2109.15170/) (2021)
- [Procedure Learning via Regularized Gromov-Wasserstein Optimal Transport](https://mlsanity.com/paper/2507.15540/) (2025)
- [REMAP: Regularized Matching and Partial Alignment of Video Embeddings](https://mlsanity.com/paper/2509.24382/) (2025)
- [Hierarchical Action Learning for Weakly-Supervised Action Segmentation](https://mlsanity.com/paper/2602.24275/) (2026)
- [Reconstructing and grounding narrated instructional videos in 3D](https://mlsanity.com/paper/2109.04409/) (2021)

## Abstract (arXiv)

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest.
