5 papers
Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention
Wensheng Wu, Zheming Lu, Ziqian Lu +5
Industrial anomaly detection faces significant challenges due to the scarcity of anomalous samples and the complexity of real-world anomalies. In this paper, we propose a foundatio…
CoreMark: Toward Robust and Universal Text Watermarking Technique
Jiale Meng, Yiming Li, Zheming Lu +3
Text watermarking schemes have gained considerable attention in recent years, yet still face critical challenges in achieving simultaneous robustness, generalizability, and imperce…
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Zewei He, Zixuan Chen, Jinlei Li +5
Recently, deep learning-based methods have dominated image dehazing domain. A multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention bloc…
MediSee: Reasoning-based Pixel-level Perception in Medical Images
Qinyue Tong, Ziqian Lu, Jun Liu +2
Despite remarkable advancements in pixel-level medical image perception, existing methods are either limited to specific tasks or heavily rely on accurate bounding boxes or text la…
Prompt-based test-time real image dehazing: a novel pipeline
Zixuan Chen, Zewei He, Ziqian Lu +2
Existing methods attempt to improve models' generalization ability on real-world hazy images by exploring well-designed training schemes (\eg, CycleGAN, prior loss). However, most…