Enabling Domain Experts to Train Efficient Few-Shot Incremental Landmark Recognition
Helmut Neuschmied, Werner Bailer · 2024
A web-based application for incremental training of landmark recognition, suitable for domain experts without machine learning expertise is presented. Its backend uses the fine-grained image classification network API-Net in a few-shot setting, making use of the two-stage fine-tuning paradigm. The aim is to enable rapid training of a classifier for recognizing new landmarks in video content, supporting needs in media production. Using base models trained on two different datasets, we demonstrate the speed and effectiveness of the application in training the networks to detect new landmarks. In addition, our application provides the ability to retrain a previously learned landmark with further data, e.g. to improve performance for views or imaging conditions not well supported by the model. This feature ensures that the model remains up-to-date with evolving datasets and environmental conditions, thereby improving its accuracy and adaptability over time.