# Adaptive Object Detection Using Adjacency and Zoom Prediction Source: [Adaptive Object Detection Using Adjacency and Zoom Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:50:53.268Z Authors: Yongxi Lu, Tara Javidi, Svetlana Lazebnik Published: 2015-12-24T04:20:16.000Z ## Abstract State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()