116 citations · 174 across the 9 of their papers we have counts for
13 papers
Boosting Active Learning via Improving Test Performance
Tianyang Wang, Xingjian Li, Pengkun Yang +5
Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless,…
DPT: Deformable Patch-based Transformer for Visual Recognition
Zhiyang Chen, Yousong Zhu, Chaoyang Zhao +4
Transformer has achieved great success in computer vision, while how to split patches in an image remains a problem. Existing methods usually use a fixed-size patch embedding which…
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Tingting Liang, Yongtao Wang, Zhi Tang +2
Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by th…
Imbalance Robust Softmax for Deep Embeeding Learning
Hao Zhu, Yang Yuan, Guosheng Hu +2
Deep embedding learning is expected to learn a metric space in which features have smaller maximal intra-class distance than minimal inter-class distance. In recent years, one rese…
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision
Yuxiang Wei, Ming Liu, Haolin Wang +3
Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pos…
Salvage Reusable Samples from Noisy Data for Robust Learning
Zeren Sun, Xian-Sheng Hua, Yazhou Yao +3
Due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images ten…