activity
20182022
most citedWhat You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction

19 citations · 73 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV20212 cited

Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation

Ailing Zeng, Xiao Sun, Lei Yang +3

Various deep learning techniques have been proposed to solve the single-view 2D-to-3D pose estimation problem. While the average prediction accuracy has been improved significantly…

cs.LG202113 cited

TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks

Yu Li, Min Li, Qiuxia Lai +2

Deep learning (DL) has achieved unprecedented success in a variety of tasks. However, DL systems are notoriously difficult to test and debug due to the lack of explainability of DL…

cs.CV2021

Skimming and Scanning for Untrimmed Video Action Recognition

Yunyan Hong, Ailing Zeng, Min Li +3

Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes. Untrimmed videos have redundant and diverse clips…

eess.SP20202 cited

T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis

Minhao Liu, Ailing Zeng, Qiuxia Lai +1

Sensor-based time series analysis is an essential task for applications such as activity recognition and brain-computer interface. Recently, features extracted with deep neural net…

cs.LG2020

DeepDyve: Dynamic Verification for Deep Neural Networks

Yu Li, Min Li, Bo Luo +2

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, how…

cs.CV202017 cited

SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach

Ailing Zeng, Xiao Sun, Fuyang Huang +3

Human poses that are rare or unseen in a training set are challenging for a network to predict. Similar to the long-tailed distribution problem in visual recognition, the small num…