# PainNet: Statistical Relation Network with Episode-Based Training for Pain Estimation

arXiv: https://arxiv.org/abs/2504.06257 · HTML: https://mlsanity.com/paper/2504.06257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition](https://mlsanity.com/paper/1907.09021/) (2019)
- [A Compare-Aggregate Model for Matching Text Sequences](https://mlsanity.com/paper/1611.01747/) (2016)
- [SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis](https://mlsanity.com/paper/1808.02531/) (2018)
- [Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions](https://mlsanity.com/paper/1706.07154/) (2017)
- [Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data](https://mlsanity.com/paper/1612.02374/) (2016)
- [AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit](https://mlsanity.com/paper/2202.12059/) (2022)
- [Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics](https://mlsanity.com/paper/2006.13882/) (2020)
- [Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices](https://mlsanity.com/paper/2209.01813/) (2022)
- [DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain](https://mlsanity.com/paper/1708.04670/) (2017)

## Abstract (arXiv)

Despite the span in estimating pain from facial expressions, limited works have focused on estimating the sequence-level pain, which is reported by patients and used commonly in clinics. In this paper, we introduce a novel Statistical Relation Network, referred to as PainNet, designed for the estimation of the sequence-level pain. PainNet employs two key modules, the embedding and the relation modules, for comparing pairs of pain videos, and producing relation scores indicating if each pair belongs to the same pain category or not. At the core of the embedding module is a statistical layer mounted on the top of a RNN for extracting compact video-level features. The statistical layer is implemented as part of the deep architecture. Doing so, allows combining multiple training stages used in previous research, into a single end-to-end training stage. PainNet is trained using the episode-based training scheme, which involves comparing a query video with a set of videos representing the different pain categories. Experimental results show the benefit of using the statistical layer and the episode-based training in the proposed model. Furthermore, PainNet outperforms the state-of-the-art results on self-reported pain estimation.