Addressing Training Bias via Automated Image Annotation
This addresses the need for large labeled datasets in computer vision, but it is incremental as it builds on existing localization and detection methods.
The paper tackles the problem of training bias in DNN models by proposing an automated image annotation system using wireless localization and cameras, demonstrating feasibility for pedestrian and vehicle detection with unspecified results.
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian and vehicle detection as examples, we demonstrate the feasibility, benefits, and challenges of an automatic image annotation system. Our work calls for new technical development on passive localization, mobile data analytics, and error-resilient ML models, as well as design issues in user privacy policies.