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20172021
most citedAdversarial Spatio-Temporal Learning for Video Deblurring

373 citations · 1.3k across the 52 of their papers we have counts for

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Showing 2020Show all

9 papers · 1 filter

cs.CV2020★ 1 cited

Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis

Wen Liu, Zhixin Piao, Zhi Tu +3

We tackle human image synthesis, including human motion imitation, appearance transfer, and novel view synthesis, within a unified framework. It means that the model, once being tr…

q-fin.RM2020★ 19 cited

Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

Xing Yan, Weizhong Zhang, Lin Ma +2

We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic…

cs.CV2020★ 19 cited

Intrinsic Relationship Reasoning for Small Object Detection

Kui Fu, Jia Li, Lin Ma +2

The small objects in images and videos are usually not independent individuals. Instead, they more or less present some semantic and spatial layout relationships with each other. M…

cs.CV2020★ 12 cited

Deblurring by Realistic Blurring

Kaihao Zhang, Wenhan Luo, Yiran Zhong +4

Existing deep learning methods for image deblurring typically train models using pairs of sharp images and their blurred counterparts. However, synthetically blurring images do not…

cs.CV2020★ 4 cited

STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition

Xu Li, Jingwen Wang, Lin Ma +4

Effective and Efficient spatio-temporal modeling is essential for action recognition. Existing methods suffer from the trade-off between model performance and model complexity. In…

cs.CV2020★ 51 cited

Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos

Yijun Song, Jingwen Wang, Lin Ma +2

The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query. Most of the existing approaches rely o…