# Prototype Learning for Micro-gesture Classification

arXiv: https://arxiv.org/abs/2408.03097 · HTML: https://mlsanity.com/paper/2408.03097/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1912.06971/) (2019)
- [PYSKL: Towards Good Practices for Skeleton Action Recognition](https://mlsanity.com/paper/2205.09443/) (2022)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [Learning Discriminative Representations for Skeleton Based Action Recognition](https://mlsanity.com/paper/2303.03729/) (2023)
- [Benchmarking Micro-action Recognition: Dataset, Methods, and Applications](https://mlsanity.com/paper/2403.05234/) (2024)
- [Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture](https://mlsanity.com/paper/2403.07347/) (2024)
- [EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer](https://mlsanity.com/paper/2312.04152/) (2023)
- [DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition](https://mlsanity.com/paper/2210.05895/) (2022)
- [Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification](https://mlsanity.com/paper/2307.10624/) (2023)
- [ViGT: Proposal-free Video Grounding with Learnable Token in Transformer](https://mlsanity.com/paper/2308.06009/) (2023)
- [Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-wise Pseudo Labeling](https://mlsanity.com/paper/2406.00919/) (2024)
- [MMAD: Multi-label Micro-Action Detection in Videos](https://mlsanity.com/paper/2407.05311/) (2024)

## Built on by (most cited first, 18 of 18)

- [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)
- [Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition](https://mlsanity.com/paper/2502.02196/) (2025)
- [CLIP-MG: Guiding Semantic Attention with Skeletal Pose Features and RGB Data for Micro-Gesture Recognition on the iMiGUE Dataset](https://mlsanity.com/paper/2506.16385/) (2025)
- [Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition](https://mlsanity.com/paper/2606.07355/) (2026)
- [Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition](https://mlsanity.com/paper/2606.09261/) (2026)
- [iMiGUE-3K: A Large-Scale Benchmark for Micro-Gesture Analysis with Self-Supervised Learning](https://mlsanity.com/paper/2605.17179/) (2026)
- [Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition](https://mlsanity.com/paper/2606.31249/) (2026)
- [MAC 2026: Advancing Micro-Action Analysis Towards Fine-Grained Understanding](https://mlsanity.com/paper/2607.16284/) (2026)
- [AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition](https://mlsanity.com/paper/2503.24057/) (2025)
- [MSF-Mamba: Motion-aware State Fusion Mamba for Efficient Micro-Gesture Recognition](https://mlsanity.com/paper/2510.10478/) (2025)
- [Multi-Track Multimodal Learning on iMiGUE: Micro-Gesture and Emotion Recognition](https://mlsanity.com/paper/2512.23291/) (2025)
- [FG-SGL: Fine-Grained Semantic Guidance Learning via Motion Process Decomposition for Micro-Gesture Recognition](https://mlsanity.com/paper/2603.16269/) (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)
- [Recognition-Conditioned Reasoning: A Training-Free Multimodal-LLM Pipeline for Fine-Grained Micro-Action Understanding](https://mlsanity.com/paper/2608.21022/) (2026)
- [SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition](https://mlsanity.com/paper/2609.08221/) (2026)

## Abstract (arXiv)

In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the track of Micro-gesture Classification in the MiGA challenge at IJCAI 2024. The task of micro-gesture classification task involves recognizing the category of a given video clip, which focuses on more fine-grained and subtle body movements compared to typical action recognition tasks. Given the inherent complexity of micro-gesture recognition, which includes large intra-class variability and minimal inter-class differences, we utilize two innovative modules, i.e., the cross-modal fusion module and prototypical refinement module, to improve the discriminative ability of MG features, thereby improving the classification accuracy. Our solution achieved significant success, ranking 1st in the track of Micro-gesture Classification. We surpassed the performance of last year's leading team by a substantial margin, improving Top-1 accuracy by 6.13%.