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Searching for small objects in large images is a task that is bothchallenging for current deep learning systems and important in numerousreal-world applications, such as remote sensing and medical imaging. Thoroughscanning of very large images is computationally expensive, particularly atresolutions sufficient to capture small objects. The smaller an object ofinterest, the more likely it is to be obscured by clutter or otherwise deemedinsignificant. We examine these issues in the context of two complementaryproblems: closed-set object detection and open-set target search. First, wepresent a method for predicting pixel-level objectness from a low resolutiongist image, which we then use to select regions for performing object detectionlocally at high resolution. This approach has the benefit of not being fixed toa predetermined grid, thereby requiring fewer costly high-resolution glimpsesthan existing methods. Second, we propose a novel strategy for open-set visualsearch that seeks to find all instances of a target class which may bepreviously unseen and is defined by a single image. We interpret both detectionproblems through a probabilistic, Bayesian lens, whereby the objectness mapsproduced by our method serve as priors in a maximum-a-posteriori approach tothe detection step. We evaluate the end-to-end performance of both thecombination of our patch selection strategy with this target search approachand the combination of our patch selection strategy with standard objectdetection methods. Both elements of our approach are seen to significantlyoutperform baseline strategies.

Addressing Visual Search in Open and Closed Set Settings