Search Results for author: Yujia Liu

Found 11 papers, 3 papers with code

PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds

no code implementations ICLR 2021 Yujia Liu, Stefano D'Aronco, Konrad Schindler, Jan Dirk Wegner

Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe.

Reconfigurable Design for Omni-adaptive Grasp Learning

2 code implementations29 Feb 2020 Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu, Sheng Ge, Chaoyang Song

Such reconfigurable design with these omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping.

Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

2 code implementations29 Feb 2020 Zeyi Yang, Sheng Ge, Fang Wan, Yujia Liu, Chaoyang Song

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment.

Rigid-Soft Interactive Learning for Robust Grasping

2 code implementations29 Feb 2020 Linhan Yang, Fang Wan, Haokun Wang, Xiaobo Liu, Yujia Liu, Jia Pan, Chaoyang Song

We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects.

Small Data Image Classification

LabelFool: A Trick in the Label Space

no code implementations25 Sep 2019 Yujia Liu, Tingting Jiang, Ming Jiang

It is widely known that well-designed perturbations can cause state-of-the-art machine learning classifiers to mis-label an image, with sufficiently small perturbations that are imperceptible to the human eyes.

A geometry-inspired decision-based attack

no code implementations ICCV 2019 Yujia Liu, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

The qFool method can drastically reduce the number of queries compared to previous decision-based attacks while reaching the same quality of adversarial examples.

General Classification Image Classification

Detection based Defense against Adversarial Examples from the Steganalysis Point of View

no code implementations CVPR 2019 Jiayang Liu, Weiming Zhang, Yiwei Zhang, Dongdong Hou, Yujia Liu, Hongyue Zha, Nenghai Yu

Moreover, secondary adversarial attacks cannot be directly performed to our method because our method is not based on a neural network but based on high-dimensional artificial features and FLD (Fisher Linear Discriminant) ensemble.

Enhanced Attacks on Defensively Distilled Deep Neural Networks

no code implementations16 Nov 2017 Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu

In this paper, we first propose the epsilon-neighborhood attack, which can fool the defensively distilled networks with 100% success rate in the white-box setting, and it is fast to generate adversarial examples with good visual quality.

Face Recognition General Classification +2

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