# ME-TST+: Micro-expression Analysis via Temporal State Transition with ROI Relationship Awareness

arXiv: https://arxiv.org/abs/2508.08082 · HTML: https://mlsanity.com/paper/2508.08082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 96 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024)
- [Long Range Language Modeling via Gated State Spaces](https://mlsanity.com/paper/2206.13947/) (2022)
- [SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series](https://mlsanity.com/paper/2403.15360/) (2024)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Micron-BERT: BERT-based Facial Micro-Expression Recognition](https://mlsanity.com/paper/2304.03195/) (2023)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [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)
- [Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition](https://mlsanity.com/paper/2006.09674/) (2020)
- [SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset](https://mlsanity.com/paper/1911.01519/) (2019)
- [Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition](https://mlsanity.com/paper/2101.04838/) (2021)
- [RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement](https://mlsanity.com/paper/2404.06483/) (2024)
- [Micro-expression detection in long videos using optical flow and recurrent neural networks](https://mlsanity.com/paper/1903.10765/) (2019)
- [3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame](https://mlsanity.com/paper/2105.06340/) (2021)
- [Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos](https://mlsanity.com/paper/2106.06489/) (2021)
- [DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition](https://mlsanity.com/paper/2301.00985/) (2023)
- [Spotting Macro- and Micro-expression Intervals in Long Video Sequences](https://mlsanity.com/paper/1912.11985/) (2019)
- [MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution](https://mlsanity.com/paper/2506.14511/) (2025)
- [Spotting Micro-Expressions on Long Videos Sequences](https://mlsanity.com/paper/1812.10306/) (2018)

## Built on by (most cited first, 1 of 1)

- [Boosting Micro-Expression Analysis via Prior-Guided Video-Level Regression](https://mlsanity.com/paper/2508.18834/) (2025)

## Abstract (arXiv)

Micro-expressions (MEs) are regarded as important indicators of an individual's intrinsic emotions, preferences, and tendencies. ME analysis requires spotting of ME intervals within long video sequences and recognition of their corresponding emotional categories. Previous deep learning approaches commonly employ sliding-window classification networks. However, the use of fixed window lengths and hard classification presents notable limitations in practice. Furthermore, these methods typically treat ME spotting and recognition as two separate tasks, overlooking the essential relationship between them. To address these challenges, this paper proposes two state space model-based architectures, namely ME-TST and ME-TST+, which utilize temporal state transition mechanisms to replace conventional window-level classification with video-level regression. This enables a more precise characterization of the temporal dynamics of MEs and supports the modeling of MEs with varying durations. In ME-TST+, we further introduce multi-granularity ROI modeling and the slowfast Mamba framework to alleviate information loss associated with treating ME analysis as a time-series task. Additionally, we propose a synergy strategy for spotting and recognition at both the feature and result levels, leveraging their intrinsic connection to enhance overall analysis performance. Extensive experiments demonstrate that the proposed methods achieve state-of-the-art performance. The codes are available at https://github.com/zizheng-guo/ME-TST.