Batch Active Learning for Deep Object Detection in Videos

Abstract: Relatively recent progress in object detection can mainly be attributed to the success of deep neural networks. However, training such models requires large amounts of annotated data. This poses a two-fold problem, namely obtaining labelled data is a time-consuming process, and training models on many instances is computationally costly. To this end a common approach is to employ active learning, which amounts to constructing a strategy to interactively query much fewer data points while maximizing the performance. In the context of deep object detection in videos, two new challenges arise. Firstly, common uncertainty-based query strategies depend on the quality of uncertainty estimates, which often require special treatment for deep neural networks. Secondly, the nature of batch-based training calls for querying subsets of images, which due to inherent temporal similarity may lack informativeness to increase performance. In this work we attempt to remedy both issues by proposing strategies relying on improved uncertainty estimates and diversification methods. Experiments show that our proposed uncertainty-based strategies are comparable to a random baseline, while the diversity-based ones, conditioned on improved uncertainty estimates, yield significantly better performance than the baseline. In particular, our best strategy using only 15% of data comes to as close as 90:27% of the performance when using all the available data to train the detector.