  Object detection is widely studied in computer vision filed. In recent years,
certain representative deep learning based detection methods along with solid
benchmarks are proposed, which boosts the development of related researchs.
However, existing detection methods still suffer from undesirable performance
under challenges such as camouflage, blur, inter-class similarity, intra-class
variance and complex environment. To address this issue, we propose LGA-RCNN
which utilizes a loss-guided attention (LGA) module to highlight representative
region of objects. Then, those highlighted local information are fused with
global information for precise classification and localization.
