4 citations · 5 across the 4 of their papers we have counts for
4 papers
BCE vs. CE in Deep Feature Learning
Qiufu Li, Huibin Xiao, Linlin Shen
When training classification models, it expects that the learned features are compact within classes, and can well separate different classes. As the dominant loss function for tra…
WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
Xinxing Cheng, Xi Jia, Wenqi Lu +4
Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense d…
UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition
Qiufu Li, Xi Jia, Jiancan Zhou +2
Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require…
Activation Template Matching Loss for Explainable Face Recognition
Huawei Lin, Haozhe Liu, Qiufu Li +1
Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionals…