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Data Priming Network for Automatic Check-Out

Introduction

This paper was accepted to ACM MM 2019.

This repository implements DPNet (Data Priming Network for Automatic Check-Out) using PyTorch 1.0.1 . This implementation is heavily influenced by the project maskrcnn-benchmark.

We propose a new data priming method to solve the domain adaptation problem. Specifically, we first use pre-augmentation data priming, in which we remove distracting background from the training images using the coarse-to-fine strategy and select images with realistic view angles by the pose pruning method. In the post-augmentation step, we train a data priming network using detection and counting collaborative learning, and select more reliable images from testing data to fine-tune the final visual item tallying network.

DPNet

Code

Source code and more details are available here.

Results

DPNet

level method cAcc mCIoU ACD mCCD mAP50 mmAP
easy Syn+Render (DPNet) 90.32% 97.87% 0.15 0.02 98.6% 83.07%
medium Syn+Render (DPNet) 80.68% 97.38% 0.32 0.03 98.07% 77.25%
hard Syn+Render (DPNet) 70.76% 97.04% 0.53 0.03 97.76% 74.95%
averaged Syn+Render (DPNet) 80.51% 97.33% 0.34 0.03 97.91% 77.04%

Model ZOO

level method cAcc model
averaged Render (DPNet) 77.91% download
averaged Syn+Render (DPNet) 80.51% download

Citations

Please cite this project in your publications if it helps your research.

@inproceedings{li2019data,
  title={Data Priming Network for Automatic Check-Out},
  author={Li, Congcong and Du, Dawei and Zhang, Libo and Luo, Tiejian and Wu, Yanjun and Tian, Qi and Wen, Longyin and Lyu, Siwei},
  booktitle={2019 ACM Multimedia Conference on Multimedia Conference},
  year={2019},
  organization={ACM}
}

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Data Priming Network for Automatic Check-Out - ACMMM 2019

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