Revolutionizing the World of Retail - New Computer Vision Challenges
The rapid development in computer vision and machine learning has caused a major disruption in the retail industry in recent years. 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 welcomes any work relating to the computer vision challenges in retail, 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
More information will be specified soon.