mlsanityData to 7 Oct 2026

Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout

arXiv 2207.05422 ↗First posted 12 Jul 2022cs.CVcited by 1

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Abstract (arXiv)

Designing an automatic checkout system for retail stores at the human level accuracy is challenging due to similar appearance products and their various poses. This paper addresses the problem by proposing a method with a two-stage pipeline. The first stage detects class-agnostic items, and the second one is dedicated to classify product categories. We also track the objects across video frames to avoid duplicated counting. One major challenge is the domain gap because the models are trained on synthetic data but tested on the real images. To reduce the error gap, we adopt domain generalization methods for the first-stage detector. In addition, model ensemble is used to enhance the robustness of the 2nd-stage classifier. The method is evaluated on the AI City challenge 2022 -- Track 4 and gets the F1 score 40% on the test A set. Code is released at the link https://github.com/cybercore-co-ltd/aicity22-track4.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 39 references that are in the map. First posted on arXiv 12 Jul 2022.

Builds on

33 of 39 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  7. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  8. 2017Mask R-CNNcited by 7,259
  9. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  10. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  11. 2016Learning without Forgettingcited by 2,465
  12. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
21 more
  1. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  2. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  3. 2017Focal Loss for Dense Object Detectioncited by 749
  4. 2018Overcoming catastrophic forgetting with hard attention to the taskcited by 672
  5. 2021Domain Generalization with MixStylecited by 563
  6. 2019Stand-Alone Self-Attention in Vision Modelscited by 542
  7. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  8. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  9. 2019Res2Net: A New Multi-scale Backbone Architecturecited by 468
  10. 2020Learning to Generate Novel Domains for Domain Generalizationcited by 296
  11. 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 230
  12. 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
  13. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  14. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 183
  15. 2019Scale-Aware Trident Networks for Object Detectioncited by 159
  16. 2020Probabilistic Anchor Assignment with IoU Prediction for Object Detectioncited by 150
  17. 2019COCO-GAN: Generation by Parts via Conditional Coordinatingcited by 67
  18. 2019Precise Detection in Densely Packed Scenescited by 62
  19. 2022The 6th AI City Challengecited by 23
  20. 20201st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentationcited by 10
  21. 2020A Solution to Product detection in Densely Packed Scenescited by 4

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Training with Product Digital Twins for AutoRetail Checkoutcited by 4