Watch talks via the links below:
- Opening Remarks, Ehud Barnea and Pan Pan and Avi Ben-Cohen
- Challenges summary, Ehud Barnea and Pan Pan and Avi Ben-Cohen
- Amazon Go – “Just Walk Out” Technology, Gerard Medioni
- A complete AI system for real-world retail problems, Ziv Mhabary
- Alibaba’s online-offline connection: Making our models efficient, compact and deployable, Lihi Zelnik-Manor
- Modeling the resistance of neural networks to label noise, Shai Avidan
- Vision for Fashion: from Personalized Recommendations to World-Wide Style Trends, Kristen Grauman
- Product Detection and Its Model Compression and Acceleration: A Practice, Zhouchen Lin
- 1st in recognition challenge: Winner Solution for AliProducts Challenge: Large-scale Product Recognition, Yuanzhi Liang and Wei Zhang
- 2nd in recognition challenge: An Effective Margin-based Product Recognition Solution to the CVPR 2020 RetailVision Challenge, Qi Xin et al.
- 3rd in recognition challenge: Large-scale Product Recognition with Loss-guided Data Refinement, Qixin Yan and Haoyu Xu and Kuangshi Zhang
Detection challenge technical reports:
- 1st: A Solution for Product Detection in Densely Packed Scenes, Jun Yu et al.
- 2nd: Working with Scale: 2nd Place Solution to Product Detection in Densely Packed Scenes, Artem Kozlov
Recognition challenge technical reports:
- 1st: Winner Solution for AliProducts Challenge: Large-scale Product Recognition, Yuanzhi Liang and Wei Zhang
- 2nd: An Effective Margin-based Product Recognition Solution to the CVPR 2020 RetailVision Challenge, Qi Xin et al.
- 3rd: Large-scale Product Recognition with Loss-guided Data Refinement, Qixin Yan and Haoyu Xu and Kuangshi Zhang
- 4th: Solution for AliProducts Challenge: Large-scale Product Recognition, Yunbo Peng and Yixin Chen and Yue Lin
- 6th: CVPR 2020 AliProducts Challenge Technical Report, Mingliang Zhangy and Baole Wei and Yirong Yang
The rapid development in computer vision and machine learning has caused a major disruption in the retail industry. In addition to the rise of the web and online shopping, traditional markets also quickly embrace AI-related technology solutions at the physical store level. Following the introduction of computer vision to the world of retail a new set challenges emerged, such as the detection of products in crowded store displays, fine-grained classification of many visually similar classes, as well as dynamically adapting to changes in data in terms of class appearance variation over time, and new classes that may appear in the images before they are labeled in the dataset. The scene complexity, scale, class imbalance, lack of reliable supervised samples, and dynamic nature of the data, encourage solutions such as context based detection and classification, few-shot learning, uncertainty modeling and open set recognition, and so forth.This workshop aims to present and progress the revolution that is already occuring in the word of retail and welcomes any work on relevant computer vision challenges, including but not limited to:
- - Detection in densely packed scenes
- - Class imbalance and lack of labeled data. New classes introduced over time
- - Ultrafine-grained object classification: Classes are often virtually indistinguishable by visual appearance
- - Hierarchical classification: products fall into product, brand, and sub-brand hierarchies
- - Context modeling of geometric structures
- - Multi-person tracking
- - Recognition of actions such as taking/returning/examining products
We invite submissions of papers limited to 8 pages according to the CVPR format. Reviews are double blind and papers will be selected based on relevance, significance and clarity. Selected papers will be presented in the website and poster session and may be invited to give a talk at the workshop. Authors of accepted papers will be asked to post their submissions on arXiv. These papers will not be included in the proceedings of CVPR 2020. To submit a paper please email it together with author details to Ehud Barnea (see organizers section below).
The workshop includes two challenges representing the difficulties of product detection and recognition "in the wild". Challenge competitors are invited to submit a paper presenting their approach and results. Please note that you do not have to submit a paper to participate in the challenges.
The world of retail takes the detection scenario to unexplored territories with millions of possible facets and hundreds of heavily crowded objects per image. This detection challenge is based on Trax’s data of supermarket shelves and pushes the limits of detection systems.
The AliProducts dataset consists of ∼3M images of ∼50K different products. The dataset covers many categories of daily commodities, including cosmetics, beverages, snacks, etc. The products are in SKU (Stock Keeping Unit) fine-grained level, and it may be difficult to distinguish between some of the products. Handling class imbalance and noisy training data are also highlights of this challenge.
March 16, 2020
Detection challenge training data released, recognition challenge sample set of the training data released
March 31, 2020
Recognition challenge entire training data released
May 28, 2020, 10:00 UTC+8
Paper submissions deadline, detection challenge test data released, recognition challenge registration deadline (competition goes live!)
May 31, 2020, 10:00 UTC+8
Recognition challenge test data released
Jun 4, 2020, 10:00 UTC+8
Detection challenge submission deadline, Recognition competitions ends
Jun 6, 2020
Paper announcements, private announcement to detection challenge participants and to the recognition challenge winner teams (public announcement will take place in the workshop)
June 15, 2020
CVPR 2020 Workshop
For questions about the workshop or to submit papers please contact Ehud Barnea (ehudb at traxretail dot com). For questions about the challenges please see challenge pages.