Age Estimation
71 papers with code • 16 benchmarks • 19 datasets
Age Estimation is the task of estimating the age of a person from an image some other kind of data.
( Image credit: BridgeNet )
Libraries
Use these libraries to find Age Estimation models and implementationsMost implemented papers
OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt
In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment.
Robust Optimization for Deep Regression
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection.
An All-In-One Convolutional Neural Network for Face Analysis
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.
Quantifying Facial Age by Posterior of Age Comparisons
We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels.
Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model
Automatic age estimation from real-world and unconstrained face images is rapidly gaining importance.
Age Estimation Using Expectation of Label Distribution Learning
Age estimation performance has been greatly improved by using convolutional neural network.
C3AE: Exploring the Limits of Compact Model for Age Estimation
Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models.
Brain age prediction using deep learning uncovers associated sequence variants
Machine learning algorithms can be trained to estimate age from brain structural MRI.
Competing Ratio Loss for Discriminative Multi-class Image Classification
However, during the training of the deep convolutional neural network, the value of NLLR is not always positive or negative, which severely affects the convergence of NLLR.