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20092023
most citedDropEdge: Towards Deep Graph Convolutional Networks on Node Classification

649 citations · 4.6k across the 115 of their papers we have counts for

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Showing 2018 · cs.CVShow all

10 papers · 2 filters

cs.CV2018★ 62 cited

Weakly Supervised Dense Event Captioning in Videos

Xuguang Duan, Wenbing Huang, Chuang Gan +3

Dense event captioning aims to detect and describe all events of interest contained in a video. Despite the advanced development in this area, existing methods tackle this task by…

cs.CV2018★ 23 cited

Progressive Feature Alignment for Unsupervised Domain Adaptation

Chaoqi Chen, Weiping Xie, Wenbing Huang +5

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discri…

cs.CV2018★ 160 cited

Discrimination-aware Channel Pruning for Deep Neural Networks

Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang +5

Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimi…

cs.CV2018★ 13 cited

Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss

Yong Guo, Qi Chen, Jian Chen +5

Deep neural networks have exhibited promising performance in image super-resolution (SR) due to the power in learning the non-linear mapping from low-resolution (LR) images to high…

cs.CV2018★ 225 cited

Adaptive Sampling Towards Fast Graph Representation Learning

Wenbing Huang, Tong Zhang, Yu Rong +1

Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scala…

cs.CV2018★ 2 cited

Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

Lijie Fan, Wenbing Huang, Chuang Gan +2

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip…