Multi-label X-ray Imagery Classification via Bottom-up Attention and Meta Fusion

Benyi Hu, Chi Zhang, Le Wang, Qilin Zhang, Yuehu Liu; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Automatic security inspection has received increasing interests in recent years. Due to the fixed top-down perspective of X-ray scanning of often tightly packed luggages, such images typically suffer from penetration-induced occlusions, severe object overlapping and violent changes in appearance. For this particular application, few research efforts have been made. To deal with the overlapping in X-ray images classification, we propose a novel Security X-ray Multi-label Classification Network (SXMNet). Our hypothesis is that different overlapping levels and scale variations are the primary challenges in the multi-label classification problem of prohibited items. To address these challenges, we propose to incorporate 1) spatial attention to locate prohibited items despite shape, color and texture variations; and 2) anisotropic fusion of per-stage predictions to dynamically fuse hierarchical visual information under violent variations. Motivated by these, our SXMNet is boosted by bottom-up attention and neural-guided Meta Fusion. Raw input image is exploited to generate high-quality attention masks in a bottom-up way for pyramid feature refinement. Subsequently, the per-stage predictions according to the refined features are automatically re-weighted and fused via a soft selection guided by neural knowledge. Comprehensive experiments on the Security Inspection X-ray (SIXray) and Occluded Prohibited Items X-ray (OPIXray) datasets demonstrate the superiority of the proposed method.