# Meta-Auxiliary Learning for Micro-Expression Recognition

arXiv: https://arxiv.org/abs/2404.12024 · HTML: https://mlsanity.com/paper/2404.12024/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [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)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [Few-Shot Image Recognition by Predicting Parameters from Activations](https://mlsanity.com/paper/1706.03466/) (2017)
- [Auxiliary Learning by Implicit Differentiation](https://mlsanity.com/paper/2007.02693/) (2020)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [Less is More: Micro-expression Recognition from Video using Apex Frame](https://mlsanity.com/paper/1606.01721/) (2016)
- [Towards Unbiased Visual Emotion Recognition via Causal Intervention](https://mlsanity.com/paper/2107.12096/) (2021)
- [Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos](https://mlsanity.com/paper/2208.01954/) (2022)
- [A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics](https://mlsanity.com/paper/1805.02397/) (2018)
- [Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis & Application](https://mlsanity.com/paper/1601.04805/) (2016)
- [A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework](https://mlsanity.com/paper/1901.07765/) (2019)

## Built on by (most cited first, 3 of 3)

- [Neuromodulated Meta-Learning](https://mlsanity.com/paper/2411.06746/) (2024)
- [Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition](https://mlsanity.com/paper/2412.13541/) (2024)
- [Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis](https://mlsanity.com/paper/2509.09251/) (2025)

## Abstract (arXiv)

Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, despite its wide applications in various scenarios, micro-expression recognition (MER) remains a challenging problem in real life due to three reasons, including (i) data-level: lack of data and imbalanced classes, (ii) feature-level: subtle, rapid changing, and complex features of MEs, and (iii) decision-making-level: impact of individual differences. To address these issues, we propose a dual-branch meta-auxiliary learning method, called LightmanNet, for fast and robust micro-expression recognition. Specifically, LightmanNet learns general MER knowledge from limited data through a dual-branch bi-level optimization process: (i) In the first level, it obtains task-specific MER knowledge by learning in two branches, where the first branch is for learning MER features via primary MER tasks, while the other branch is for guiding the model obtain discriminative features via auxiliary tasks, i.e., image alignment between micro-expressions and macro-expressions since their resemblance in both spatial and temporal behavioral patterns. The two branches of learning jointly constrain the model of learning meaningful task-specific MER knowledge while avoiding learning noise or superficial connections between MEs and emotions that may damage its generalization ability. (ii) In the second level, LightmanNet further refines the learned task-specific knowledge, improving model generalization and efficiency. Extensive experiments on various benchmark datasets demonstrate the superior robustness and efficiency of LightmanNet.