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Found 9 papers, 8 papers with code
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art
Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis.
HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation
In this study, we proposed a Heterogeneous Swin Transformer with Multi-Receptive Field (HST-MRF) model based on U-shaped networks for medical image segmentation.
Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis
We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical.
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering
With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning.
Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations
Bilateral postural symmetry plays a key role as a potential risk marker for autism spectrum disorder (ASD) and as a symptom of congenital muscular torticollis (CMT) in infants, but current methods of assessing symmetry require laborious clinical expert assessments.
InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild
We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders.
Invariant Representation Learning for Infant Pose Estimation with Small Data
However, while the applications of human pose estimation have become more and more broad, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatility of their poses.
Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions
Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation.
Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture Analysis
We have evaluated our method on videos collected from several infants during their NNS behaviors and we have achieved the quantified NNS patterns closely comparable to results from visual inspection as well as contact-based sensor readings.
