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20172020
most citedCollaborative Training of Balanced Random Forests for Open Set Domain Adaptation

8 citations · 18 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CV2020

OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems

Changhee Won, Hochang Seok, Zhaopeng Cui +2

In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, w…

cs.CV20208 cited

Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation

Jongbin Ryu, Jiun Bae, Jongwoo Lim

In this paper, we introduce a collaborative training algorithm of balanced random forests with convolutional neural networks for domain adaptation tasks. In real scenarios, most do…

cs.CV2019

OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching

Changhee Won, Jongbin Ryu, Jongwoo Lim

In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with u…

cs.CV20194 cited

ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems

Hochang Seok, Jongwoo Lim

In this paper we propose a robust visual odometry system for a wide-baseline camera rig with wide field-of-view (FOV) fisheye lenses, which provides full omnidirectional stereo obs…

cs.CV2019

SweepNet: Wide-baseline Omnidirectional Depth Estimation

Changhee Won, Jongbin Ryu, Jongwoo Lim

Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind re…

cs.CV20176 cited

Tracking Persons-of-Interest via Unsupervised Representation Adaptation

Shun Zhang, Jia-Bin Huang, Jongwoo Lim +4

Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in sc…