Driving scene retrieval by example from large-scale data

Paper i proceeding, 2019

Many machine learning approaches train networks with input from large datasets to reach high task performance. Collected datasets, such as Berkeley Deep Drive Video (BDD-V) for autonomous driving, contain a large variety of scenes and hence features. However, depending on the
task, subsets, containing certain features more densely, support training better than others. For example, training networks on tasks such as image segmentation, bounding box detection or tracking requires an ample amount of objects in the input data. When training a network to perform optical flow estimation from first-person video, over-proportionally many straight driving scenes in the training data may lower generalization to turns. Even though some scenes of the BDD-V dataset are labeled with scene, weather or time of day information, these may be too coarse to filter the dataset best for a particular training task. Furthermore, even defining an exhaustive list of good label-types is complicated as it requires choosing the most relevant concepts of the natural world for a task. Alternatively, we investigate how to use examples of desired data to retrieve more similar data from a large-scale dataset. Following the paradigm of ”I know it when I see it”, we present a deep learning approach to use driving examples for retrieving similar scenes from the BDD-V dataset. Our method leverages only automatically collected labels. We show how we can reliably vary time of the day or objects in our query examples and retrieve nearest neighbors from the dataset. Using this method, already collected data can be filtered to remove bias from a dataset, removing scenes regarded too redundant to train on.