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20132021
most citedHuman Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction

38 citations · 41 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CV2021

TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video

Mohsen Gholami, Ahmad Rezaei, Helge Rhodin +2

Estimating 3D human poses from video is a challenging problem. The lack of 3D human pose annotations is a major obstacle for supervised training and for generalization to unseen da…

cs.CV2021

Multi-view 3D Reconstruction with Transformer

Dan Wang, Xinrui Cui, Xun Chen +5

Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these me…

cs.CV2021

Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection

Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1

Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception. Using these point cloud and image data, perception models based on deep neural nets (DNNs) have…

cs.CV2021

Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models

Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1

We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. DNNs have achieved h…

cs.CV20203 cited

Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification

Yongwei Wang, Mingquan Feng, Rabab Ward +2

Deep neural networks are vulnerable to adversarial attacks. White-box adversarial attacks can fool neural networks with small adversarial perturbations, especially for large size i…

cs.CV2020

Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection

Yongwei Wang, Xin Ding, Li Ding +2

Recently, generative adversarial networks (GANs) can generate photo-realistic fake facial images which are perceptually indistinguishable from real face photos, promoting research…