6 accepted papers
Visual anomaly detection is vital for quality control applications by identifying deviations from normal patterns. Previous structural or logical anomaly detection methods mainly focus on pixel-level deviations like texture defects and reconstruction errors, ignoring the object-level structural and
Citation recommendation aims to provide researchers with the most relevant references for their manuscripts, helping them swiftly discover pertinent studies and bolster the reliability of their arguments. However, some individuals manipulate these recommendation systems by injecting false informatio
Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying
Visual text generation, which aims to generate photo-realistic images with coherent and well-formed scene text being rendered, has attracted widespread attention. Although recent works have achieved promising performance, the limited flexibility and controllability hinder their practical application…
Shadow characteristics are of great importance for scene understanding. Existing works mainly consider shadow regions as binary masks, often leading to imprecise detection results and suboptimal performance for scene understanding. We demonstrate that such an assumption oversimplifies light-…
Federated learning (FL) learns an optimal global model by aggregating local models trained on distributed data from different devices. Due to heterogeneous data distributions across devices, local models will be divergent, resulting in the global model’s performance degradation. Recent studies attem…