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Yue Wu
Researcher at Northeastern University
Publications - 29
Citations - 3492
Yue Wu is an academic researcher from Northeastern University. The author has contributed to research in topics: Convolutional neural network & Cluster analysis. The author has an hindex of 15, co-authored 29 publications receiving 1707 citations. Previous affiliations of Yue Wu include Beijing University of Posts and Telecommunications.
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MMDetection: Open MMLab Detection Toolbox and Benchmark.
Kai Chen,Jiaqi Wang,Jiangmiao Pang,Yuhang Cao,Yu Xiong,Xiaoxiao Li,Shuyang Sun,Wansen Feng,Ziwei Liu,Jiarui Xu,Zheng Zhang,Dazhi Cheng,Chenchen Zhu,Tianheng Cheng,Qijie Zhao,Buyu Li,Xin Lu,Rui Zhu,Yue Wu,Jifeng Dai,Jingdong Wang,Jianping Shi,Wanli Ouyang,Chen Change Loy,Dahua Lin +24 more
TL;DR: This paper presents MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules, and conducts a benchmarking study on different methods, components, and their hyper-parameters.
Proceedings ArticleDOI
Large Scale Incremental Learning
TL;DR: This work found that the last fully connected layer has a strong bias towards the new classes, and this bias can be corrected by a linear model, and with two bias parameters, this method performs remarkably well on two large datasets.
Proceedings ArticleDOI
Rethinking Classification and Localization for Object Detection
TL;DR: In this paper, the authors proposed a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression, achieving an accuracy of +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet101 backbones, respectively.
Posted Content
Rethinking Classification and Localization for Object Detection
TL;DR: A Double-Head method is proposed, which has a fully connected head focusing on classification and a convolution head for bounding box regression, and it is found that fc-head has more spatial sensitivity than conv-head.
Journal ArticleDOI
Visual Kinship Recognition of Families in the Wild
TL;DR: It is shown that pre-trained CNN models fine-tuned on FIW outscores other conventional methods and achieved state-of-the art on the renowned KinWild datasets and is statistically compare FIW to related datasets, which unarguably shows enormous gains in overall size and amount of information encapsulated in the labels.