5 papers · 1 filter
CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors
Mingyuan Li, Tong Jia, Hao Wang +5
Prohibited item detection based on X-ray images is one of the most effective security inspection methods. However, the foreground-background feature coupling caused by the overlapp…
MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection
Mingyuan Li, Tong Jia, Hui Lu +7
Unlike natural images with occlusion-based overlap, X-ray images exhibit depth-induced superimposition and semi-transparent appearances, where objects at different depths overlap a…
FOAM: A General Frequency-Optimized Anti-Overlapping Framework for Overlapping Object Perception
Mingyuan Li, Tong Jia, Han Gu +7
Overlapping object perception aims to decouple the randomly overlapping foreground-background features, extracting foreground features while suppressing background features, which…
CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels
Shizhuo Deng, Bowen Han, Jiaqi Chen +3
Noisy labels threaten the robustness of few-shot learning (FSL) due to the inexact features in a new domain. CLIP, a large-scale vision-language model, performs well in FSL on imag…
Open-Vocabulary X-ray Prohibited Item Detection via Fine-tuning CLIP
Shuyang Lin, Tong Jia, Hao Wang +3
X-ray prohibited item detection is an essential component of security check and categories of prohibited item are continuously increasing in accordance with the latest laws. Previo…