The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations

arXiv:2609.04009 · cs.CV, cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/open-mmlab/mmpose

Terminology

• Pose Estimation — 21× in this paper
• Clustering — 12× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Agnostic Learning — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation
• The Influence of Faulty Labels in Data Sets on Human Pose Estimation
• High-Resolution Representations for Labeling Pixels and Regions
• Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
• Early Stopping Against Label Noise Without Validation Data
• Understanding deep learning requires rethinking generalization

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