Demo Abstract: BiGuide: A Bi-level Data Acquisition Guidance for Object Detection on Mobile Devices

Abstract

Real-time object detection (OD) is a key enabling technology for a wide range of emerging mobile system applications. However, deploying an OD model pre-trained on a public dataset (source domain) in a specific local environment (target domain) is known to lead to significant performance degradation because of the so-called domain gap between the dataset and the environment. Collecting local data and fine-tuning the OD model on this data is a commonly used approach for improving the robustness of OD models in real-world deployments. Yet, the question of how to collect this data is currently largely overlooked; unsupported data collection is likely to produce datasets that contain significant proportion of redundant or uninformative data for model training. In this demo, we present BiGuide, a bi-level image data acquisition guidance for OD tasks, to guide users to change their camera locations or angles to different extents (significantly or slightly) to obtain the data which benefits model training via image-level and object instance-level guidance. We showcase an interactive demonstration of collecting data for a lemur species detection application we are developing and deploying at the Duke Lemur Center. Demo participants will take pictures of lemur toys with the mobile phone under the real-time guidance and will observe the real-time display of the metrics that assess the importance of the captured data. They will develop an intuition for how real-time image importance assessment and bi-level guidance improve the quality of collected data.