# MMAD: Multi-label Micro-Action Detection in Videos

arXiv: https://arxiv.org/abs/2407.05311 · HTML: https://mlsanity.com/paper/2407.05311/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 86 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 30 of 50)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [A Short Note on the Kinetics-700 Human Action Dataset](https://mlsanity.com/paper/1907.06987/) (2019)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://mlsanity.com/paper/1911.11462/) (2019)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [Learning Salient Boundary Feature for Anchor-free Temporal Action Localization](https://mlsanity.com/paper/2103.13137/) (2021)
- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [TriDet: Temporal Action Detection with Relative Boundary Modeling](https://mlsanity.com/paper/2303.07347/) (2023)
- [Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos](https://mlsanity.com/paper/1507.05738/) (2015)
- [Fast Learning of Temporal Action Proposal via Dense Boundary Generator](https://mlsanity.com/paper/1911.04127/) (2019)
- [Diagnosing Error in Temporal Action Detectors](https://mlsanity.com/paper/1807.10706/) (2018)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [FineAction: A Fine-Grained Video Dataset for Temporal Action Localization](https://mlsanity.com/paper/2105.11107/) (2021)
- [Benchmarking Micro-action Recognition: Dataset, Methods, and Applications](https://mlsanity.com/paper/2403.05234/) (2024)
- [End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames](https://mlsanity.com/paper/2311.17241/) (2023)
- [MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection](https://mlsanity.com/paper/2112.03902/) (2021)
- [Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data](https://mlsanity.com/paper/1502.06648/) (2015)

## Built on by (most cited first, 20 of 20)

- [Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition](https://mlsanity.com/paper/2412.14719/) (2024)
- [Prototype Learning for Micro-gesture Classification](https://mlsanity.com/paper/2408.03097/) (2024)
- [MM-Gesture: Towards Precise Micro-Gesture Recognition through Multimodal Fusion](https://mlsanity.com/paper/2507.08344/) (2025)
- [Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention](https://mlsanity.com/paper/2507.09512/) (2025)
- [Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition](https://mlsanity.com/paper/2507.21977/) (2025)
- [XInsight: Integrative Stage-Consistent Psychological Counseling Support Agents for Digital Well-Being](https://mlsanity.com/paper/2603.06583/) (2026)
- [MA-Bench: Towards Fine-grained Micro-Action Understanding](https://mlsanity.com/paper/2603.26586/) (2026)
- [BVINet: Unlocking Blind Video Inpainting with Zero Annotations](https://mlsanity.com/paper/2502.01181/) (2025)
- [Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition](https://mlsanity.com/paper/2606.09261/) (2026)
- [Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition](https://mlsanity.com/paper/2606.31249/) (2026)
- [Prompt-Aware Controllable Shadow Removal](https://mlsanity.com/paper/2501.15043/) (2025)
- [Text-guided Fine-Grained Video Anomaly Understanding](https://mlsanity.com/paper/2511.00524/) (2025)
- [MSF-Mamba: Motion-aware State Fusion Mamba for Efficient Micro-Gesture Recognition](https://mlsanity.com/paper/2510.10478/) (2025)
- [Evidence Packing for Cross-Domain Image Deepfake Detection with LVLMs](https://mlsanity.com/paper/2603.17761/) (2026)
- [B-MoE: A Body-Part-Aware Mixture-of-Experts "All Parts Matter" Approach to Micro-Action Recognition](https://mlsanity.com/paper/2603.24245/) (2026)
- [Motion Reinforces Appearance: RGB-Skeleton Gated Residual Fusion for Micro-Gesture Online Recognition](https://mlsanity.com/paper/2606.11645/) (2026)
- [A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition](https://mlsanity.com/paper/2606.13030/) (2026)
- [A New Multi-Domain Benchmark for Micro-Action Recognition and Detection](https://mlsanity.com/paper/2606.14096/) (2026)
- [Zero-MELO: Test-Time Evidence Calibration with Multimodal LLMs for Zero-Shot Micro-Gesture Recognition](https://mlsanity.com/paper/2608.14854/) (2026)
- [SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition](https://mlsanity.com/paper/2609.08221/) (2026)

## Abstract (arXiv)

Human body actions are an important form of non-verbal communication in social interactions. This paper specifically focuses on a subset of body actions known as micro-actions, which are subtle, low-intensity body movements with promising applications in human emotion analysis. In real-world scenarios, human micro-actions often temporally co-occur, with multiple micro-actions overlapping in time, such as concurrent head and hand movements. However, current research primarily focuses on recognizing individual micro-actions while overlooking their co-occurring nature. To address this gap, we propose a new task named Multi-label Micro-Action Detection (MMAD), which involves identifying all micro-actions in a given short video, determining their start and end times, and categorizing them. Accomplishing this requires a model capable of accurately capturing both long-term and short-term action relationships to detect multiple overlapping micro-actions. To facilitate the MMAD task, we introduce a new dataset named Multi-label Micro-Action-52 (MMA-52) and propose a baseline method equipped with a dual-path spatial-temporal adapter to address the challenges of subtle visual change in MMAD. We hope that MMA-52 can stimulate research on micro-action analysis in videos and prompt the development of spatio-temporal modeling in human-centric video understanding. The proposed MMA-52 dataset is available at: https://github.com/VUT-HFUT/Micro-Action.