activity
20152022
most citedJoint Monocular 3D Vehicle Detection and Tracking

49 citations · 382 across the 44 of their papers we have counts for

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Showing 2018 · cs.CVShow all

10 papers · 2 filters

cs.CV2018★ 11 cited

Self-Supervised Learning of Depth and Camera Motion from 360° Videos

Fu-En Wang, Hou-Ning Hu, Hsien-Tzu Cheng +5

As 360° cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360° perception becomes more and more important. We propose a novel self…

cs.CV2018★ 6 cited

DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama

Shang-Ta Yang, Fu-En Wang, Chi-Han Peng +3

We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama. To achieve better prediction accuracy, our method leve…

cs.CV2018★ 49 cited

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.CV2018★ 2 cited

Unsupervised Stylish Image Description Generation via Domain Layer Norm

Cheng Kuan Chen, Zhu Feng Pan, Min Sun +1

Most of the existing works on image description focus on generating expressive descriptions. The only few works that are dedicated to generating stylish (e.g., romantic, lyric, etc…

cs.CV2018

Liquid Pouring Monitoring via Rich Sensory Inputs

Tz-Ying Wu, Juan-Ting Lin, Tsun-Hsuang Wang +3

Humans have the amazing ability to perform very subtle manipulation task using a closed-loop control system with imprecise mechanics (i.e., our body parts) but rich sensory informa…

cs.CV2018

Leveraging Motion Priors in Videos for Improving Human Segmentation

Yu-Ting Chen, Wen-Yen Chang, Hai-Lun Lu +2

Despite many advances in deep-learning based semantic segmentation, performance drop due to distribution mismatch is often encountered in the real world. Recently, a few domain ada…