Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
This addresses localization errors in object detection for computer vision applications, representing an incremental improvement over existing methods.
The paper tackled inaccurate localization in object detection by using Bayesian optimization for candidate regions and a structured loss for training, improving performance on PASCAL VOC datasets and outperforming previous state-of-the-art when combined.
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.