mlsanityData to 7 Oct 2026

Active Transfer Learning for Efficient Video-Specific Human Pose Estimation

arXiv 2311.05041 ↗First posted 8 Nov 2023cs.CV cs.LGcited by 4

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Abstract (arXiv)

Human Pose (HP) estimation is actively researched because of its wide range of applications. However, even estimators pre-trained on large datasets may not perform satisfactorily due to a domain gap between the training and test data. To address this issue, we present our approach combining Active Learning (AL) and Transfer Learning (TL) to adapt HP estimators to individual video domains efficiently. For efficient learning, our approach quantifies (i) the estimation uncertainty based on the temporal changes in the estimated heatmaps and (ii) the unnaturalness in the estimated full-body HPs. These quantified criteria are then effectively combined with the state-of-the-art representativeness criterion to select uncertain and diverse samples for efficient HP estimator learning. Furthermore, we reconsider the existing Active Transfer Learning (ATL) method to introduce novel ideas related to the retraining methods and Stopping Criteria (SC). Experimental results demonstrate that our method enhances learning efficiency and outperforms comparative methods. Our code is publicly available at: https://github.com/ImIntheMiddle/VATL4Pose-WACV2024

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 36 of its 68 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

36 of 68 references.

References · 36

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  4. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
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  11. 2019Diverse mini-batch Active Learningcited by 88
  12. 2019Sim2real transfer learning for 3D human pose estimation: motion to the rescuecited by 75
24 more
  1. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 72
  2. 2021OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Associationcited by 55
  3. 2017Detect-and-Track: Efficient Pose Estimation in Videoscited by 51
  4. 2021Deep Dual Consecutive Network for Human Pose Estimationcited by 51
  5. 2019Learning Temporal Pose Estimation from Sparsely-Labeled Videoscited by 39
  6. 2021Test-Time Personalization with a Transformer for Human Pose Estimationcited by 33
  7. 2022DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimationcited by 29
  8. 2022JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Trackingcited by 25
  9. 2014A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stoppingcited by 21
  10. 2018Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Networkcited by 20
  11. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  12. 2019Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classificationcited by 15
  13. 2022Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognitioncited by 14
  14. 2021DeepAL: Deep Active Learning in Pythoncited by 13
  15. 2023Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registrationcited by 13
  16. 2020Key Frame Proposal Network for Efficient Pose Estimation in Videoscited by 12
  17. 2020Towards human-level performance on automatic pose estimation of infant spontaneous movementscited by 10
  18. 2021Transfer Learning for Pose Estimation of Illustrated Characterscited by 10
  19. 2022OTPose: Occlusion-Aware Transformer for Pose Estimation in Sparsely-Labeled Videoscited by 10
  20. 2022VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimationcited by 9
  21. 2019Semi- and Weakly-supervised Human Pose Estimationcited by 8
  22. 2020Deep Reinforcement Learning for Active Human Pose Estimationcited by 7
  23. 2015Analysis of Stopping Active Learning based on Stabilizing Predictionscited by 5
  24. 2021Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Trainingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Physical Plausibility-aware Trajectory Prediction via Locomotion Embodimentcited by 10
  2. 2025StepAL: Step-aware Active Learning for Cataract Surgical Videoscited by 2
  3. 2025MMCM: Multimodality-aware Metric using Clustering-based Modes for Probabilistic Human Motion Predictioncited by 1
  4. 2026Human-in-the-loop Adaptation in Group Activity Feature Learning for Team Sports Video Retrievalcited by 0