← Age And Gender Classification
Papers with Code · CC BY-SA 4.0
Adience Age
아카이브 평가 16건 · 마지막 적재/변경
평가 기록
과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.
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원본 기록 순서 · 순위 미부여
| 모델 | 원본 지표·값 | 논문 | 구현 |
|---|
| ViT-hSeq |
- Accuracy (5-fold)
- 84.91
|
A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images
2024-03-19 |
연결 없음 |
| MiVOLO-V2 |
- Accuracy (5-fold)
- 69.43
|
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation
2024-03-04 |
코드 1 |
| MiVOLO-D1 |
- Accuracy (5-fold)
- 68.69
|
MiVOLO: Multi-input Transformer for Age and Gender Estimation
2023-07-10 |
코드 1 · 코드 2 |
| AL-ResNets-34 + IMDB-WIKI |
- Accuracy (5-fold)
- 67.47
|
Fine-Grained Age Estimation in the wild with Attention LSTM Networks
2018-05-26 |
연결 없음 |
| R-SAAFc2 +IMDB-WIKI |
- Accuracy (5-fold)
- 67.3
|
ConvNets with Smooth Adaptive Activation Functions for Regression
2017-01-01 |
연결 없음 |
| RoR-34 + IMDB-WIKI |
- Accuracy (5-fold)
- 66.74
|
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
2017-10-09 |
연결 없음 |
| MWR |
- Accuracy (5-fold)
- 62.6
|
Moving Window Regression: A Novel Approach to Ordinal Regression
2022-03-24 |
코드 1 |
| UNIORD-ResNet-101 (single crop, pytorch) |
- Accuracy (5-fold)
- 61
|
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
2020-11-15 |
연결 없음 |
| RetinaFace + ArcFace + MLP + IC + Skip connections |
- Accuracy (5-fold)
- 60.86
|
Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
2021-08-18 |
코드 1 |
| CPG (single crop, pytorch) |
- Accuracy (5-fold)
- 57.66
|
Compacting, Picking and Growing for Unforgetting Continual Learning
2019-10-15 |
코드 1 · 코드 2 |
| PAENet (single crop, tensorflow) |
- Accuracy (5-fold)
- 57.3
|
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
2019-06-10 |
코드 1 · 코드 2 |
| MegaAge |
- Accuracy (5-fold)
- 56.01
|
Quantifying Facial Age by Posterior of Age Comparisons
2017-08-31 |
코드 1 |
| Levi_Hassner CNN (over-sample, caffe) |
- Accuracy (5-fold)
- 50.7
|
Age and Gender Classification using Convolutional Neural Networks
2015-10-26 |
코드 1 · 코드 2 |
| Levi_Hassner CNN (single crop, caffe) |
- Accuracy (5-fold)
- 49.5
|
Age and Gender Classification using Convolutional Neural Networks
2015-10-26 |
코드 1 · 코드 2 |
| LMTCNN-2-1 (single crop, tensorflow) |
- Accuracy (5-fold)
- 44.26
|
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
2018-06-06 |
코드 1 |
| Levi_Hassner CNN (single crop, tensorflow) |
- Accuracy (5-fold)
- 44.14
|
Age and Gender Classification using Convolutional Neural Networks
2015-10-26 |
코드 1 · 코드 2 |