Cross-Domain Fine-Grained Classification: A Review

Fine-grained classification is an interesting but challenging task due to the high amount of data needed to achieve a high accuracy. However, the high specificity of the classes makes it difficult to collect a large amount of samples. Thus, the use of cross-domain learning is an interesting aspect since an abundant amount of data exists for some domains like web images exists. In this review, the current works of cross-domain fine-grained classification are summarized and potential areas for future work are highlighted. Even though first works exist, the variety of methods is still small and interesting cross-domain settings are rarely considered. Thus, the field of cross-domain fine-grained classification provides a large room for future research

A Baseline for Cross-Domain Fine-Grained Vehicle Classification in a Supervised Partially Zero-Shot Setting

Fine-grained vehicle classification is an important task particularly for security applications like searching for cars of suspects who abuse stolen license plates. However, data privacy and the large number of existing car models render it highly difficult to create a large up-to-date dataset for fine-grained vehicle classification with surveillance images. While a large number of images of vehicles are available in the web due to car selling sites, they have a perspective which is vastly different to surveillance images. Domain adaptation is the field of research that uses domain-wise inappropriate images for training of classification models with the target of running accurate inference on images of a different domain. Since the widely considered unsupervised and semi-supervised domain adaptation settings are unrealistic for fine-grained vehicle classification, we establish a baseline for cross-domain fine-grained vehicle classification in a supervised partially zero-shot setting. Our results indicate that existing domain adaptation methods like domain adversarial training and triplet loss are still advantageous for this setting and we show the benefit of distance-based classification for this task

A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition

When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.info:eu-repo/semantics/publishedVersio

Deep learning for retail product recognition: challenges and techniques

Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields

Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop\u27s results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports