activity
20182022
most citedDeep Deformation Detail Synthesis for Thin Shell Models

2 citations · 3 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Gradient Concealment: Free Lunch for Defending Adversarial Attacks

Sen Pei, Jiaxi Sun, Xiaopeng Zhang +1

Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks. However, even the \emph{state-of-the-art} deep learning based classifiers are…

cs.CV20212 cited

Deep Deformation Detail Synthesis for Thin Shell Models

Lan Chen, Lin Gao, Jie Yang +4

In physics-based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data-driven techniques make…

cs.CV2020

Circumventing Outliers of AutoAugment with Knowledge Distillation

Longhui Wei, An Xiao, Lingxi Xie +3

AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper se…

cs.GR2020

MGCN: Descriptor Learning using Multiscale GCNs

Yiqun Wang, Jing Ren, Dong-Ming Yan +3

We propose a novel framework for computing descriptors for characterizing points on three-dimensional surfaces. First, we present a new non-learned feature that uses graph wavelets…

cs.CV2018

Hardware-Efficient Guided Image Filtering For Multi-Label Problem

Longquan Dai, Mengke Yuan, Zechao Li +2

The Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an n x n matrix for each pixel. To t…

cs.CV2018

Speeding Up the Bilateral Filter: A Joint Acceleration Way

Longquan Dai, Mengke Yuan, Xiaopeng Zhang

Computational complexity of the brute-force implementation of the bilateral filter (BF) depends on its filter kernel size. To achieve the constant-time BF whose complexity is irrel…