# Learning from Label Relationships in Human Affect

arXiv: https://arxiv.org/abs/2207.05577 · HTML: https://mlsanity.com/paper/2207.05577/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Long-Term Feature Banks for Detailed Video Understanding](https://mlsanity.com/paper/1812.05038/) (2018)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Pre-training strategies and datasets for facial representation learning](https://mlsanity.com/paper/2103.16554/) (2021)
- [The OMG-Emotion Behavior Dataset](https://mlsanity.com/paper/1803.05434/) (2018)
- [MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition](https://mlsanity.com/paper/1911.09784/) (2019)
- [SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis](https://mlsanity.com/paper/1808.02531/) (2018)
- [A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild](https://mlsanity.com/paper/1805.01452/) (2018)
- [Towards General Models of Player Experience: A Study Within Genres](https://mlsanity.com/paper/2110.00978/) (2021)
- [Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding](https://mlsanity.com/paper/1707.04555/) (2017)
- [Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation](https://mlsanity.com/paper/2011.00043/) (2020)
- [Learning Grimaces by Watching TV](https://mlsanity.com/paper/1610.02255/) (2016)
- [A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues](https://mlsanity.com/paper/1805.00638/) (2018)
- [Modeling Dynamics of Facial Behavior for Mental Health Assessment](https://mlsanity.com/paper/2108.09934/) (2021)

## Built on by (most cited first, 8 of 8)

- [Prompting Visual-Language Models for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2308.13382/) (2023)
- [Attribute-preserving Face Dataset Anonymization via Latent Code Optimization](https://mlsanity.com/paper/2303.11296/) (2023)
- [EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition](https://mlsanity.com/paper/2310.16640/) (2023)
- [Contrastive Language-Image Learning with Augmented Textual Prompts for 3D/4D FER Using Vision-Language Model](https://mlsanity.com/paper/2504.19739/) (2025)
- [Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition](https://mlsanity.com/paper/2505.09336/) (2025)
- [Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image](https://mlsanity.com/paper/2411.15799/) (2024)
- [Self-Supervised Multi-View Representation Learning using Vision-Language Model for 3D/4D Facial Expression Recognition](https://mlsanity.com/paper/2506.01203/) (2025)
- [Facial Emotion Learning with Text-Guided Multiview Fusion via Vision-Language Model for 3D/4D Facial Expression Recognition](https://mlsanity.com/paper/2507.01673/) (2025)

## Abstract (arXiv)

Human affect and mental state estimation in an automated manner, face a number of difficulties, including learning from labels with poor or no temporal resolution, learning from few datasets with little data (often due to confidentiality constraints) and, (very) long, in-the-wild videos. For these reasons, deep learning methodologies tend to overfit, that is, arrive at latent representations with poor generalisation performance on the final regression task. To overcome this, in this work, we introduce two complementary contributions. First, we introduce a novel relational loss for multilabel regression and ordinal problems that regularises learning and leads to better generalisation. The proposed loss uses label vector inter-relational information to learn better latent representations by aligning batch label distances to the distances in the latent feature space. Second, we utilise a two-stage attention architecture that estimates a target for each clip by using features from the neighbouring clips as temporal context. We evaluate the proposed methodology on both continuous affect and schizophrenia severity estimation problems, as there are methodological and contextual parallels between the two. Experimental results demonstrate that the proposed methodology outperforms all baselines. In the domain of schizophrenia, the proposed methodology outperforms previous state-of-the-art by a large margin, achieving a PCC of up to 78% performance close to that of human experts (85%) and much higher than previous works (uplift of up to 40%). In the case of affect recognition, we outperform previous vision-based methods in terms of CCC on both the OMG and the AMIGOS datasets. Specifically for AMIGOS, we outperform previous SoTA CCC for both arousal and valence by 9% and 13% respectively, and in the OMG dataset we outperform previous vision works by up to 5% for both arousal and valence.