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.
Source code and more details are available here.
| 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% |
| level | method | cAcc | model |
|---|---|---|---|
| averaged | Render (DPNet) | 77.91% | download |
| averaged | Syn+Render (DPNet) | 80.51% | download |
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}
}