198 citations · 688 across the 92 of their papers we have counts for
10 papers · 1 filter
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…
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
Boshen Zhang, Yuxi Li, Yuanpeng Tu +5
Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to…
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
Zhengkai Jiang, Yuxi Li, Ceyuan Yang +4
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Ada…
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Jiangning Zhang, Xiangtai Li, Yabiao Wang +4
Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical evolutionary algorithm (EA) and derives that both…
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…
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation
Zhengkai Jiang, Zhangxuan Gu, Jinlong Peng +6
Video Instance Segmentation (VIS) is a task that simultaneously requires classification, segmentation, and instance association in a video. Recent VIS approaches rely on sophistica…
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…