MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection

The recent publication titled 'MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection' presents a novel approach to enhance the detection of overlapping objects in X-ray images. Unlike natural images, X-ray images often feature depth-induced superimposition, complicating the identification of prohibited items. The MMCL framework aims to correct the distribution of content queries, which represent object hypotheses, thus improving the detection process. By employing a multi-class exclusion loss and a min-margin clustering loss, the framework achieves better intra-class diversity and inter-class separability. Evaluated on datasets like PIXray, OPIXray, and PIDray, the method demonstrates state-of-the-art performance, marking a significant advancement in the field of computer vision and security applications.