activity
20182020
most citedLearning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

14 citations · 22 across the 2 of their papers we have counts for

collaborators

7 papers

cs.CV2020★ 8 cited

Long-term Human Motion Prediction with Scene Context

Zhe Cao, Hang Gao, Karttikeya Mangalam +3

Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine…

cs.LG2019★ 14 cited

Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou

In this work, we consider one challenging training time attack by modifying training data with bounded perturbation, hoping to manipulate the behavior (both targeted or non-targete…

cs.CV2019

Monocular Plan View Networks for Autonomous Driving

Dequan Wang, Coline Devin, Qi-Zhi Cai +2

Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth. We propose a simple transformation…

cs.CV2018

Disentangling Propagation and Generation for Video Prediction

Hang Gao, Huazhe Xu, Qi-Zhi Cai +3

A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated.…

cs.CV2018

Joint Monocular 3D Vehicle Detection and Tracking

Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang +5

Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper,…

cs.AI2018

Deep Object-Centric Policies for Autonomous Driving

Dequan Wang, Coline Devin, Qi-Zhi Cai +2

While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For robotics tasks, such as auton…