182 citations · 461 across the 8 of their papers we have counts for
8 papers
The 6th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +14
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and art…
Accelerating Neutron Scattering Data Collection and Experiments Using AI Deep Super-Resolution Learning
Ming-Ching Chang, Yi Wei, Wei-Ren Chen +1
We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely u…
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches
Yuezun Li, Xiao Bian, Ming-ching Chang +1
Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on explor…
Robust Adversarial Perturbation on Deep Proposal-based Models
Yuezun Li, Daniel Tian, Ming-Ching Chang +2
Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) met…
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition
Wenbo Li, Ming-Ching Chang, Siwei Lyu
We present a bootstrapping framework to simultaneously improve multi-person tracking and activity recognition at individual, interaction and social group activity levels. The infer…
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
Yuezun Li, Ming-Ching Chang, Siwei Lyu
The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we descri…