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

arXiv: https://arxiv.org/abs/2609.04009 · HTML: https://mlsanity.com/paper/2609.04009/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Combating noisy labels by agreement: A joint training method with co-regularization](https://mlsanity.com/paper/2003.02752/) (2020)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Sample Selection with Uncertainty of Losses for Learning with Noisy Labels](https://mlsanity.com/paper/2106.00445/) (2021)
- [C-Mixup: Improving Generalization in Regression](https://mlsanity.com/paper/2210.05775/) (2022)
- [Early Stopping Against Label Noise Without Validation Data](https://mlsanity.com/paper/2502.07551/) (2025)
- [Invariant Representation Learning for Infant Pose Estimation with Small Data](https://mlsanity.com/paper/2010.06100/) (2020)
- [Late Stopping: Avoiding Confidently Learning from Mislabeled Examples](https://mlsanity.com/paper/2308.13862/) (2023)
- [SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking](https://mlsanity.com/paper/2502.11534/) (2025)
- [AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation](https://mlsanity.com/paper/2205.05277/) (2022)
- [ScarceNet: Animal Pose Estimation with Scarce Annotations](https://mlsanity.com/paper/2303.15023/) (2023)
- [Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors](https://mlsanity.com/paper/1909.09823/) (2019)
- [Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features](https://mlsanity.com/paper/2212.09321/) (2022)
- [Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers](https://mlsanity.com/paper/1902.08068/) (2019)
- [Automatic infant 2D pose estimation from videos: comparing seven deep neural network methods](https://mlsanity.com/paper/2406.17382/) (2024)
- [Differences Between Hard and Noisy-labeled Samples: An Empirical Study](https://mlsanity.com/paper/2307.10718/) (2023)
- [Sample Selection via Contrastive Fragmentation for Noisy Label Regression](https://mlsanity.com/paper/2502.17771/) (2025)
- [ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory](https://mlsanity.com/paper/2507.18183/) (2025)
- [Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data](https://mlsanity.com/paper/2205.01892/) (2022)
- [The Influence of Faulty Labels in Data Sets on Human Pose Estimation](https://mlsanity.com/paper/2409.03887/) (2024)
- [Comparison of marker-less 2D image-based methods for infant pose estimation](https://mlsanity.com/paper/2410.04980/) (2024)
- [Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation](https://mlsanity.com/paper/2504.05789/) (2025)

## Built on by (most cited first, 1 of 1)

- [Toward Reliable Infant Pose Estimation: A Training-Dynamics Approach to Noisy Annotation Detection](https://mlsanity.com/paper/2610.06423/) (2026)

## Abstract (arXiv)

Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose corruption can severely impair model performance. Although robustness to label noise has been extensively studied for classification tasks, it remains relatively underexplored in Pose Estimation (PE). This limitation becomes critical in clinical contexts, including neonatology, where PE of preterm infants is used to support the assessment of spontaneous motility, a key indicator of neurodevelopmental trajectories. In such settings, infants' images labeling is further hindered by visual challenges (e.g., keypoint self-occlusions, caregiver interference), making the annotation process inherently susceptible to errors. To tackle noisy annotations in PE, we introduce REliable keypoint selection via Memory of traINing Dynamics (REMIND), a clustering-based keypoint-selection strategy that exploits keypoint-wise training dynamics to identify noisy labels without assuming any prior knowledge of the noise distribution, thus enabling noise-free model training. When evaluated on the proprietary NeoPose dataset, comprising 46 videos of 46 preterm infants recorded in real clinical settings, REMIND correctly identifies noisy annotations across multiple corruption scenarios, achieving up to 93% Area Under the Curve (AUC) with three different PE architectures used in the relevant literature. To our knowledge, this is the first study to explicitly address label noise in preterm infants' PE, paving the way for the design of trustworthy learning-based algorithms for infants'monitoring support when data quality cannot be guaranteed.