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20182020
most citedTwo-Level Residual Distillation based Triple Network for Incremental Object Detection

17 citations · 34 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CV2020

Gaussian Constrained Attention Network for Scene Text Recognition

Zhi Qiao, Xugong Qin, Yu Zhou +2

Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we a…

cs.CV202017 cited

Two-Level Residual Distillation based Triple Network for Incremental Object Detection

Dongbao Yang, Yu Zhou, Dayan Wu +3

Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data. Due to time consumpt…

cs.CV20204 cited

Self-Training for Domain Adaptive Scene Text Detection

Yudi Chen, Wei Wang, Yu Zhou +3

Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a…

cs.CV201913 cited

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Dmitrii Marin, Zijian He, Peter Vajda +4

Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. Ho…

cs.CV2018

Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation

Xi Peng, Zhiqiang Tang, Fei Yang +2

Random data augmentation is a critical technique to avoid overfitting in training deep neural network models. However, data augmentation and network training are usually treated as…