.png){kind=icon}
.png){kind=icon}
Return
Iterative Knowledge Distillation for Automatic Check-Out
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2020.3037502.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Automatic Check-Out (ACO) provides an object detection based mechanism for retailers to process the purchases of customers automatically. However, it suffers a lot from the domain shift problem because of different data distribution between the single item in training exemplar images and mixed items in testing checkout images. In this paper, we propose a new iterative knowledge distillation method to solve the domain adaptation problem for this task. First, we develop a new augmentation data strategy to generate synthesized checkout images. It can extract segmented items from the training images by the coarse-to-fine strategy and filter items with unrealistic poses by pose pruning. Second, we propose a dual pyramid scale network (DPSNet) to exploit the multi-scale feature representation in joint detection and counting views. Third, the iterative knowledge distillation training strategy is developed to make full use of both image-level and instance-level samples to narrow the semantic gap between source domain and target domain. Extensive experiments on the large-scale Retail Product Checkout (RPC) dataset show the proposed DPSNet can achieve state-of-the-art performance compared with existing methods. The source codes can be found at https://isrc.iscas.ac.cn/gitlab/research/dpsnet.
Keywords:
Testing
Training
Adaptation models
Reliability
Feature extraction
Training data
Task analysis
Automatic check-out
domain adaptation
data augmentation
dual pyramid scale network
iterative knowledge distillation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W
Organization
View more organizations