60 citations · 115 across the 12 of their papers we have counts for
6 papers · 1 filter
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
Yucong Lin, Jinhua Su, Yuhang Li +12
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. P…
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
Yang Shen, Xuhao Sun, Xiu-Shen Wei +2
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-g…
3D Siamese Transformer Network for Single Object Tracking on Point Clouds
Le Hui, Lingpeng Wang, Linghua Tang +3
Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance…
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
Mu He, Le Hui, Yikai Bian +3
Existing self-supervised monocular depth estimation methods can get rid of expensive annotations and achieve promising results. However, these methods suffer from severe performanc…
OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation
Jianjun Qian, Shumin Zhu, Chaoyu Zhao +2
Face representation in the wild is extremely hard due to the large scale face variations. To this end, some deep convolutional neural networks (CNNs) have been developed to learn d…
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection
Hongcheng Guo, Xingyu Lin, Jian Yang +5
Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps). Considering log data of variant domains, retraining the whole network fo…