# A Full Transformer-based Framework for Automatic Pain Estimation using Videos

arXiv: https://arxiv.org/abs/2412.15095 · HTML: https://mlsanity.com/paper/2412.15095/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)

## Built on by (most cited first, 11 of 11)

- [Efficient Pain Recognition via Respiration Signals: A Single Cross-Attention Transformer Multi-Window Fusion Pipeline](https://mlsanity.com/paper/2507.21886/) (2025)
- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://mlsanity.com/paper/2505.01571/) (2025)
- [Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image](https://mlsanity.com/paper/2507.21881/) (2025)
- [Tiny-BioMoE: a Lightweight Embedding Model for Biosignal Analysis](https://mlsanity.com/paper/2507.21875/) (2025)
- [A Lightweight Transformer for Pain Recognition from Brain Activity](https://mlsanity.com/paper/2604.16491/) (2026)
- [A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities](https://mlsanity.com/paper/2607.19716/) (2026)
- [ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment](https://mlsanity.com/paper/2607.19722/) (2026)
- [One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment](https://mlsanity.com/paper/2605.00856/) (2026)
- [ReMiX-MAE: Learning Missing-Channel Cross-Modal Representations from RGB-Only Clinical Facial Videos for Sympathetic-Mediated Pain Assessment](https://mlsanity.com/paper/2608.02561/) (2026)
- [FUSE: Frame-Unified Stress Estimation from Facial Video](https://mlsanity.com/paper/2608.10442/) (2026)
- [UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing](https://mlsanity.com/paper/2609.29553/) (2026)

## Abstract (arXiv)

The automatic estimation of pain is essential in designing an optimal pain management system offering reliable assessment and reducing the suffering of patients. In this study, we present a novel full transformer-based framework consisting of a Transformer in Transformer (TNT) model and a Transformer leveraging cross-attention and self-attention blocks. Elaborating on videos from the BioVid database, we demonstrate state-of-the-art performances, showing the efficacy, efficiency, and generalization capability across all the primary pain estimation tasks.