# Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation

arXiv: https://arxiv.org/abs/2308.16552 · HTML: https://mlsanity.com/paper/2308.16552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 67 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [Unsupervised Learning from Narrated Instruction Videos](https://mlsanity.com/paper/1506.09215/) (2015)
- [Alleviating Over-segmentation Errors by Detecting Action Boundaries](https://mlsanity.com/paper/2007.06866/) (2020)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation](https://mlsanity.com/paper/1602.02995/) (2016)
- [Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation](https://mlsanity.com/paper/2003.02824/) (2020)
- [Connectionist Temporal Modeling for Weakly Supervised Action Labeling](https://mlsanity.com/paper/1607.08584/) (2016)
- [An end-to-end generative framework for video segmentation and recognition](https://mlsanity.com/paper/1509.01947/) (2015)
- [Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation](https://mlsanity.com/paper/2209.00638/) (2022)
- [Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos](https://mlsanity.com/paper/2203.14104/) (2022)
- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Temporal Relational Modeling with Self-Supervision for Action Segmentation](https://mlsanity.com/paper/2012.07508/) (2020)
- [MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/2006.09220/) (2020)
- [Temporal Action Segmentation with High-level Complex Activity Labels](https://mlsanity.com/paper/2108.06706/) (2021)
- [Cross-Enhancement Transformer for Action Segmentation](https://mlsanity.com/paper/2205.09445/) (2022)
- [Do we really need temporal convolutions in action segmentation?](https://mlsanity.com/paper/2205.13425/) (2022)
- [RCMNet: A deep learning model assists CAR-T therapy for leukemia](https://mlsanity.com/paper/2205.04230/) (2022)
- [Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference](https://mlsanity.com/paper/2205.04227/) (2022)
- [Neuro-Symbolic Learning: Principles and Applications in Ophthalmology](https://mlsanity.com/paper/2208.00374/) (2022)

## Built on by (most cited first, 4 of 4)

- [Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition](https://mlsanity.com/paper/2408.03867/) (2024)
- [Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection](https://mlsanity.com/paper/2403.02307/) (2024)
- [Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques](https://mlsanity.com/paper/2411.03131/) (2024)
- [MAJORScore: A Novel Metric for Evaluating Multimodal Relevance via Joint Representation](https://mlsanity.com/paper/2509.21365/) (2025)

## Abstract (arXiv)

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a custom CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, thereby devising an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone of the model preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which accounts for the excavation of the segmentation pipeline on the CPR dataset. In general, we unprecedentedly probe into a feasible pipeline that genuinely elevates the CPR instruction qualification via action segmentation in conjunction with cutting-edge deep learning techniques. Associated experiments advocate our implementation with multiple metrics surpassing 91.0%.