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Micro-gesture Online Recognition using Learnable Query Points

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arxiv 2407.04490 v1 pith:WVH5WDMD submitted 2024-07-05 cs.CV

Micro-gesture Online Recognition using Learnable Query Points

Pengyu Liu , Fei Wang , Kun Li , Guoliang Chen , Yanyan Wei , Shengeng Tang , Zhiliang Wu , Dan Guo This is my paper
classification cs.CV
keywords micro-gestureonlinerecognitiontaskmicro-gesturessolutionstarttimes
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track in the MiGA challenge at IJCAI 2024. The Micro-gesture Online Recognition task involves identifying the category and locating the start and end times of micro-gestures in video clips. Compared to the typical Temporal Action Detection task, the Micro-gesture Online Recognition task focuses more on distinguishing between micro-gestures and pinpointing the start and end times of actions. Our solution ranks 2nd in the Micro-gesture Online Recognition track.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition

    cs.CV 2026-06 unverdicted novelty 5.0

    A decoupled adapter with independent spatial-temporal branches via depthwise convolutions and a dynamic augmentation strategy for long-tail data achieves first place with F1 0.43808 in a micro-gesture recognition challenge.

  2. Micro-DualNet: Dual-Path Spatio-Temporal Network for Micro-Action Recognition

    cs.CV 2026-04 unverdicted novelty 5.0

    Micro-DualNet employs dual ST and TS pathways with entity-level adaptive routing and Mutual Action Consistency loss to achieve competitive results on MA-52 and state-of-the-art on iMiGUE for micro-action recognition.

  3. A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition

    cs.CV 2026-06 unverdicted novelty 4.0

    A multi-modal system combining skeleton/heatmap/RGB features with cross-modal pseudo-labeling and semantic losses achieves 68.13% F1-score and 4th place on the MiGA-IJCAI micro-gesture challenge.

  4. Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition

    cs.CV 2026-06 unverdicted novelty 4.0

    Ensemble of self-supervised RGB model and supervised models achieves new SOTA of 74.419% on iMiGUE micro-gesture dataset.

  5. MAC 2026: Advancing Micro-Action Analysis Towards Fine-Grained Understanding

    cs.CV 2026-07 conditional novelty 3.0

    MAC 2026 reports a three-track micro-action challenge, adding a fine-grained MLLM-based understanding track evaluated on MA-Bench, with top-3 leaderboard results for each track.

  6. Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition

    cs.CV 2026-06 unverdicted novelty 3.0

    A competition-winning multi-modal model for hidden emotion recognition integrates static and dynamic pose features via cross-attention and MIL pooling while noting representation collapse in vision foundation models o...