# Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction Source: [Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:16:31.033Z Authors: Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee Published: 2015-04-13T18:50:51.000Z ## Abstract Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon high-capacity CNN architectures, we address the localization problem by 1) using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and 2) training the CNN with a structured loss that explicitly penalizes the localization inaccuracy. In experiments, we demonstrated that each of the proposed methods improves the detection performance over the baseline method on PASCAL VOC 2007 and 2012 datasets. Furthermore, two methods are complementary and significantly outperform the previous state-of-the-art when combined. ## 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]()