37 citations · 196 across the 17 of their papers we have counts for
23 papers
Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning
Chengming Xu, Chen Liu, Siqian Yang +4
Positive-Unlabeled (PU) learning aims to learn a model with rare positive samples and abundant unlabeled samples. Compared with classical binary classification, the task of PU lear…
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
Chengming Xu, Chen Liu, Xinwei Sun +4
The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It…
FRIH: Fine-grained Region-aware Image Harmonization
Jinlong Peng, Zekun Luo, Liang Liu +6
Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image. Existing methods perform the same harmonization process for the…
Learning Distinctive Margin toward Active Domain Adaptation
Ming Xie, Yuxi Li, Yabiao Wang +6
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning…
ASFD: Automatic and Scalable Face Detector
Jian Li, Bin Zhang, Yabiao Wang +6
Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However,…
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution
Jiangning Zhang, Chao Xu, Jian Li +4
In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-samp…