Facial Attribute Classification
18 papers with code • 7 benchmarks • 10 datasets
Facial attribute classification is the task of classifying various attributes of a facial image - e.g. whether someone has a beard, is wearing a hat, and so on.
( Image credit: Multi-task Learning of Cascaded CNN for Facial Attribute Classification )
Benchmarks
These leaderboards are used to track progress in Facial Attribute Classification
Datasets
Most implemented papers
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.
Deep Learning Face Attributes in the Wild
LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction.
Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.
FaceXFormer: A Unified Transformer for Facial Analysis
Unlike these conventional methods, our FaceXformer leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework.
PANDA: Pose Aligned Networks for Deep Attribute Modeling
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion.
Adversarial Information Factorization
We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation.
Imbalanced Deep Learning by Minority Class Incremental Rectification
In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data.
Fair Contrastive Learning for Facial Attribute Classification
Through extensive experiments on CelebA and UTK Face, we validate that the proposed method significantly outperforms SupCon and existing state-of-the-art methods in terms of the trade-off between top-1 accuracy and fairness.
Label2Label: A Language Modeling Framework for Multi-Attribute Learning
As each sample is annotated with multiple attribute labels, these "words" will naturally form an unordered but meaningful "sentence", which depicts the semantic information of the corresponding sample.
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
By training in an alternate manner, the discoverer tries to find multiple unknown biases of the classifier without any annotations of biases, and the classifier aims at unlearning the biases identified by the discoverer.