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20212023
most citedSamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

4 citations · 6 across the 5 of their papers we have counts for

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5 papers

cs.CV2023

SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines

Shizun Wang, Weihong Zeng, Xu Wang +7

The creation of a parameterized stylized character involves careful selection of numerous parameters, also known as the "avatar vectors" that can be interpreted by the avatar engin…

cs.CV2022

Efficient Meta-Tuning for Content-aware Neural Video Delivery

Xiaoqi Li, Jiaming Liu, Shizun Wang +6

Recently, Deep Neural Networks (DNNs) are utilized to reduce the bandwidth and improve the quality of Internet video delivery. Existing methods train corresponding content-aware su…

cs.CV2022

Adaptive Patch Exiting for Scalable Single Image Super-Resolution

Shizun Wang, Jiaming Liu, Kaixin Chen +3

Since the future of computing is heterogeneous, scalability is a crucial problem for single image super-resolution. Recent works try to train one network, which can be deployed on…

cs.CV2021★ 4 cited

SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

Shizun Wang, Ming Lu, Kaixin Chen +4

With the development of Deep Neural Networks (DNNs), plenty of methods based on DNNs have been proposed for Single Image Super-Resolution (SISR). However, existing methods mostly t…

eess.IV2021★ 2 cited

Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation

Jiaming Liu, Ming Lu, Kaixin Chen +7

Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwi…