Browse State-of-the-Art › Facial Attribute Classification
Facial Attribute Classification
18 papers with code · 7 benchmarks · 10 datasets archive 2025-07-28
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 )
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| LFWA (7 rows) | Label2Label | Label2Label: A Language Modeling Framework for Multi-Attribute Learning | code | Syntology ran 2 of 5 samples · 3 unverified | Compare |
| bFFHQ (3 rows) | DebiAN | Discover and Mitigate Unknown Biases with Debiasing Alternate Networks | code | Syntology ran 2 of 9 samples · 7 unverified | Compare |
| FairFace (3 rows) | MiVOLO-V2 | Beyond Specialization: Assessing the Capabilities of MLLMs in Age... | code | — | Compare |
| CelebV-HQ (1 row) | MARLIN | MARLIN: Masked Autoencoder for facial video Representation LearnINg | code | — | Compare |
| DiveFace (1 row) | Neighbour Learning | Deep Generative Views to Mitigate Gender Classification Bias... | — | — | Compare |
| MORPH (1 row) | Neighbour Learning | Deep Generative Views to Mitigate Gender Classification Bias... | — | — | Compare |
| UTKFace (1 row) | Neighbour Learning | Deep Generative Views to Mitigate Gender Classification Bias... | — | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
10 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
18 shown of 18 papers with code (37 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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14 Aug 2019 7 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.
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19 Mar 2024 2 repositories listedIn this work, we introduce FaceXFormer, an end-to-end unified transformer model capable of performing nine facial analysis tasks including face parsing, landmark detection, head pose estimation, attribute prediction,…
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10 Jul 2023 2 repositories listed Syntology ran 2 of 2 samples · 0 unverifiedAge and gender recognition in the wild is a highly challenging task: apart from the variability of conditions, pose complexities, and varying image quality, there are cases where the face is partially or completely…
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6 Jul 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.
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28 Nov 2014 2 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction.
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29 Aug 2024 1 repository listedExisting bias mitigation techniques typically require demographic annotations and often obtain a trade-off between fairness and accuracy, i.
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11 Mar 2024 1 repository listed Syntology ran 10 of 11 samples · 1 unverified · 11 pointer-only (licence)This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation.
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4 Mar 2024 1 repository listedFurthermore, we attempted various ways to fine-tune the ShareGPT4V model for this specific task, aiming to achieve state-of-the-art results in this particular challenge.
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3 Nov 2023 1 repository listedRecently, various studies have presented machine unlearning algorithms and evaluated their methods on several datasets.
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12 Nov 2022 1 repository listedThis paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial…
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13 Oct 2022 1 repository listedTwo annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially…
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11 Oct 2022 1 repository listedNeural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize.
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20 Jul 2022 1 repository listed Syntology ran 2 of 9 samples · 7 unverified · 9 pointer-only (licence)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.
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18 Jul 2022 1 repository listed Syntology ran 2 of 5 samples · 3 unverified · 5 pointer-only (licence)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.
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30 Mar 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)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…
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28 Apr 2018 1 repository listedIn 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.
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14 Nov 2017 1 repository listedWe propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation.
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21 Nov 2013 1 repository listedWe 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.
Syntology lines on 8 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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