1 code implementation • 7 Dec 2023 • Yunhan Zhao, Haoyu Ma, Shu Kong, Charless Fowlkes
We explore this problem by first introducing a new benchmark dataset, consisting of RGB and depth videos, per-frame camera pose, and instance-level annotations in both 2D camera and 3D world coordinates.
1 code implementation • 30 Oct 2023 • Qianqian Shen, Yunhan Zhao, Nahyun Kwon, Jeeeun Kim, Yanan Li, Shu Kong
Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene.
no code implementations • 20 Jan 2022 • Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, Charless Fowlkes
Our approach achieves state-of-the-art performance on both RealEstate10K and MannequinChallenge dataset with large baselines, complex geometry and extreme camera motions.
1 code implementation • CVPR 2021 • Yunhan Zhao, Shu Kong, Charless Fowlkes
We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses.
no code implementations • CVPR 2020 • Yunhan Zhao, Shu Kong, Daeyun Shin, Charless Fowlkes
In this setting, we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data.
no code implementations • 16 May 2018 • Yunhan Zhao, Ye Tian, Charless Fowlkes, Wei Shen, Alan Yuille
Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.
no code implementations • 6 Dec 2017 • Yunhan Zhao, Haider Ali, Rene Vidal
This work pushes the limit of unsupervised domain adaptation through an in-depth evaluation of several state of the art methods on benchmark datasets and the new dataset suite.