551 citations · 1k across the 71 of their papers we have counts for
22 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…
RankDNN: Learning to Rank for Few-shot Learning
Qianyu Guo, Hongtong Gong, Xujun Wei +4
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classifica…
Self-supervised Amodal Video Object Segmentation
Jian Yao, Yuxin Hong, Chiyu Wang +6
Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information t…
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
Yuqian Fu, Yu Xie, Yanwei Fu +2
Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core cha…
Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification
Renjie Zhang, Yu Fang, Huaxin Song +4
Cloth changing person re-identification(Re-ID) can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques and is therefore extremely…