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DLDL-MatConvNet

This repository is a MatConvNet re-implementation of "Deep Label Distribution Learning with Label Ambiguity", Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, Xin Geng. The paper is accepted at [IEEE Trans. Image Processing (TIP), 2017].

You can train Deep ConvNets from Scratch or a pre-trained model on your datasets with limited samples and ambiguous labels. This repo is created by Bin-Bin Gao.

PWC

PWC

PWC

PWC

PWC

PWC

PWC

PWC

PWC

Feature visualization

Table of Contents

  1. Facial Age Estimation
  2. Head Pose Estimation
  3. Multi-label Classification
  4. Semantic Segmentation

Facial Age Estimation

step1: download pre-trained model to ./DLDLModel

step2: in matlab, run age-demo.m

Pre-trained models:

Dataset Model MAE epsilon-error
ChaLearn15 DLDL 5.34(exp) 0.44
ChaLearn15 DLDL+VGG-Face 3.51(exp) 0.31
Morph DLDL 2.51±0.03 (max) -
Morph DLDL+VGG-Face 2.42±0.01 (max) -

Head Pose Estimation

step1: download pre-trained model to ./DLDLModel

step2: in matlab, run pose-demo.m

Pre-trained models:

Dataset Model Pitch Yaw Pitch+Yaw Pitch Yaw Pitch+Yaw
Pointing’04 DLDL 1.69±0.32 3.16±0.07 4.64±0.24 91.65±1.13 79.57±0.57 73.15±0.72
BJUT-3D DLDL 0.02±0.01 0.07±0.01 0.09±0.01 99.81±0.04 99.27±0.08 99.09±0.09
AFLW DLDL 5.75 6.60 9.78 95.41 92.89 89.27

Multi-label Classification

step1: download pre-trained model to ./DLDLModel

step2: in matlab, run ml-demo.m

Single-model classification resluts (mAP in %) on VOC2007

Training Style Net-D+Max Net-D+Avg Net-E+Max Net-E+Avg
IF-DLDL 90.1 model 90.5 model 90.6 model 90.7 model
PF-DLDL 92.3 model 92.1 model 92.5 model 92.2 model

Multi-model ensemble results (mAP in %) on VOC2007 and VOC2012

Dataset Training Style mAP
VOC2007 IF-DLDL 91.1
VOC2007 PF-DLDL 93.4
VOC2012 IF-DLDL 89.9
VOC2012 PF-DLDL 92.4

Semantic Segmentation

step1: download pre-trained model to ./DLDLModel

step2: in matlab, run seg-demo.m

Dataset Model MIoU
VOC2011 DLDL-8s 64.9
VOC2011 DLDL-8s+CRF 67.6
VOC2012 DLDL-8s 64.5
VOC2012 DLDL-8S+CRF 67.1

Additional Information

If you find DLDL helpful, please cite it as

@ARTICLE{gao2016deep,
         author={Gao, Bin-Bin and Xing, Chao and Xie, Chen-Wei and Wu, Jianxin and Geng, Xin},
         title={Deep Label Distribution Learning with Label Ambiguity},
         journal={IEEE Transactions on Image Processing},
         year={2017},
         volume={26},
         number={6},
         pages={2825-2838}, 
         }

ATTN1: This packages are free for academic usage. You can run them at your own risk. For other purposes, please contact Prof. Jianxin Wu ([email protected]).