R
Rui Qian
Researcher at Google
Publications - 24
Citations - 1845
Rui Qian is an academic researcher from Google. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 10, co-authored 13 publications receiving 587 citations. Previous affiliations of Rui Qian include Cornell University & Peking University.
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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi,Yin Cui,Aravind Srinivas,Rui Qian,Tsung-Yi Lin,Ekin D. Cubuk,Quoc V. Le,Barret Zoph +7 more
TL;DR: A systematic study of the Copy-Paste augmentation for instance segmentation where the authors randomly paste objects onto an image finds that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines.
Proceedings ArticleDOI
Attentive Generative Adversarial Network for Raindrop Removal from A Single Image
TL;DR: Zhang et al. as discussed by the authors apply an attentive generative network using adversarial training to visually remove raindrops, and thus transform a raindrop degraded image into a clean one.
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Spatiotemporal Contrastive Video Representation Learning
TL;DR: This work proposes a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames, and proposes a sampling-based temporal augmentation methods to avoid overly enforcing invariance on clips that are distant in time.
Proceedings ArticleDOI
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi,Yin Cui,Aravind Srinivas,Rui Qian,Tsung-Yi Lin,Ekin D. Cubuk,Quoc V. Le,Barret Zoph +7 more
TL;DR: In this paper, the Copy-Paste method is used for instance segmentation where objects are pasted randomly onto an image. And the authors show that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines.
Posted Content
Attentive Generative Adversarial Network for Raindrop Removal from a Single Image
TL;DR: Zhang et al. as discussed by the authors apply an attentive generative network using adversarial training to visually remove raindrops, and thus transform a raindrop degraded image into a clean one.