no code implementations • 7 Jun 2024 • Peng Xing, Dong Zhang, Jinhui Tang, Zechao Li
Specifically, by Case-1, we found that the main reasons detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has-not/has been recovered into its original state.
no code implementations • 5 Jun 2024 • Peng Xing, Ning Wang, Jianbo Ouyang, Zechao Li
The remarkable advancement in text-to-image generation models significantly boosts the research in ID customization generation.
no code implementations • 18 Feb 2024 • Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang, Haojing Huang, Yangning Li, Hai-Tao Zheng, Wenhao Jiang, Ying Shen
Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences.
no code implementations • 19 Oct 2022 • Peng Xing, Hao Tang, Jinhui Tang, Zechao Li
However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and 2) the features of the teacher network serve solely as a ``reference standard" and are not fully leveraged.
no code implementations • 26 Sep 2022 • Peng Xing, Zechao Li
Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous samples.
no code implementations • 26 Sep 2022 • Peng Xing, Yanpeng Sun, Zechao Li
In this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is proposed by jointly exploring effective generation method of forged anomalous samples and the normal sample features as the guidance information of segmentation for anomaly detection.