BooDet: Gradient Boosting Object Detection With Additive Learning-Based Prediction Aggregation.

Abstract

In recent years, the community of object detection has witnessed remarkable progress with the development of deep neural networks. But the detection performance still suffers from the dilemma between complex networks and single-vector predictions. In this paper, we propose a novel approach to boost the object detection performance based on aggregating predictions. First, we propose a unified module with adjustable hyper-structure to generate multiple predictions from a single detection network. Second, we formulate the additive learning for aggregating predictions, which reduces the classification and regression losses by progressively adding the prediction values. Based on the gradient Boosting strategy, the optimization of the additional predictions is further modeled as weighted regression problems to fit the Newton-descent directions. By aggregating multiple predictions from a single network, we propose the BooDet approach which can Bootstrap the classification and bounding box regression for high-performance object Detection. In particular, we plug the BooDet into Cascade R-CNN for object detection. Extensive experiments show that the proposed approach is quite effective to improve object detection. We obtain a 1.3%~2.0% improvement over the strong baseline Cascade R-CNN on COCO val dataset. We achieve 56.5% AP on the COCO test-dev dataset with only bounding box annotations.