  Visual active search (VAS) has been proposed as a modeling framework in which
visual cues are used to guide exploration, with the goal of identifying regions
of interest in a large geospatial area. Its potential applications include
identifying hot spots of rare wildlife poaching activity, search-and-rescue
scenarios, identifying illegal trafficking of weapons, drugs, or people, and
many others. State of the art approaches to VAS include applications of deep
reinforcement learning (DRL), which yield end-to-end search policies, and
traditional active search, which combines predictions with custom algorithmic
approaches. While the DRL framework has been shown to greatly outperform
traditional active search in such domains, its end-to-end nature does not make
full use of supervised information attained either during training, or during
actual search, a significant limitation if search tasks differ significantly
from those in the training distribution. We propose an approach that combines
the strength of both DRL and conventional active search by decomposing the
search policy into a prediction module, which produces a geospatial
distribution of regions of interest based on task embedding and search history,
and a search module, which takes the predictions and search history as input
and outputs the search distribution. We develop a novel meta-learning approach
for jointly learning the resulting combined policy that can make effective use
of supervised information obtained both at training and decision time. Our
extensive experiments demonstrate that the proposed representation and
meta-learning frameworks significantly outperform state of the art in visual
active search on several problem domains.

Artificial Intelligence;Computer Vision and Pattern Recognition