The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resources. Batch active learning, which adaptively issues batched queries to a labeling oracle, is a common approach for addressing this problem. The practical benefits of batch sampling come with the downside of less adaptivity and the risk of sampling redundant examples within a batch -- a risk that grows with the batch size. In this work, we analyze an efficient active learning algorithm, which focuses on the large batch setting. In particular, we show that our sampling method, which combines notions of uncertainty and diversity, easily scales to batch sizes (100K-1M) several orders of magnitude larger than used in previous studies and provides significant improvements in model training efficiency compared to recent baselines. Finally, we provide an initial theoretical analysis, proving label complexity guarantees for a related sampling method, which we show is approximately equivalent to our sampling method in specific settings.

The analysis of videos taken by active operators recording human interactions and activities in the field presents a new set of challenges. For brevity in this paper we will call such subject centric field grade videos ad hoc videos of events. Human test subjects readily segment ad hoc videos of events into scene-like segments. These segmentations can not be replicated by the state of the art automatic video segmentation algorithms. We propose and evaluate a method to segment ad hoc videos of events into atomic semantics units. Motivated by [Bel74] we call these units sub-scenes. Our experiments show that the segments detected by human subjects are sequences of sub-scenes. Thus the sub-scenes appear to be a semantic version of the video shots that are used to piece together scenes by state of the art video segmentation algorithms.