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20172025
most citedSingle Image Super-Resolution Using Multi-Scale Convolutional Neural Network

6 citations · 9 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers

Longkun Zou, Wanru Zhu, Ke Chen +4

Semantic pattern of an object point cloud is determined by its topological configuration of local geometries. Learning discriminative representations can be challenging due to larg…

cs.CV2023

CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animation

Zhaojie Chu, Kailing Guo, Xiaofen Xing +3

Speech-driven 3D facial animation is a challenging cross-modal task that has attracted growing research interest. During speaking activities, the mouth displays strong motions, whi…

cs.CV20231 cited

Dynamic Shuffle: An Efficient Channel Mixture Method

Kaijun Gong, Zhuowen Yin, Yushu Li +2

The redundancy of Convolutional neural networks not only depends on weights but also depends on inputs. Shuffling is an efficient operation for mixing channel information but the s…

cs.CV20231 cited

LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch

Pucheng Zhai, Kailing Guo, Fang Liu +2

Structured pruning is a commonly used convolutional neural network (CNN) compression approach. Pruning rate setting is a fundamental problem in structured pruning. Most existing wo…

cs.CV2021

Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values

Zhenquan Lin, Kailing Guo, Xiaofen Xing +1

To obtain good performance, convolutional neural networks are usually over-parameterized. This phenomenon has stimulated two interesting topics: pruning the unimportant weights for…

cs.CV20176 cited

Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network

Xiaoyi Jia, Xiangmin Xu, Bolun Cai +1

Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images…