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 )
Benchmarks
These leaderboards are used to track progress in Age Estimation
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Libraries
Use these libraries to find Age Estimation models and implementations
UTKFace
MORPH
Adience
CACD
FG-NET
AADB
AgeDB
MegaAge
Most 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
raywang335/l2rclip
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NeurIPS 2023
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
bazilas/matconvnet-deepReg
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ICCV 2015
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
gaobb/DLDL-v2
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Age estimation performance has been greatly improved by using convolutional neural network.
C3AE: Exploring the Limits of Compact Model for Age Estimation
StevenBanama/C3AE
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CVPR 2019
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
benniatli/BrainAgePredictionResNet
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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.
