no code implementations • 27 Mar 2024 • Shenxing Wei, Xing Wei, Zhiheng Ma, Songlin Dong, Shaochen Zhang, Yihong Gong
Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance.
1 code implementation • ICCV 2023 • Heng Zhao, Shenxing Wei, Dahu Shi, Wenming Tan, Zheyang Li, Ye Ren, Xing Wei, Yi Yang, ShiLiang Pu
Taking the symmetry properties of objects into consideration, we design a symmetry-aware matching loss to facilitate the learning of dense point-wise geometry features and improve the performance considerably.