Search Results for author: Yunhan Zhao

Found 7 papers, 3 papers with code

Instance Tracking in 3D Scenes from Egocentric Videos

1 code implementation7 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.

Human-Object Interaction Detection Object Tracking

A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture

1 code implementation30 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.

Object object-detection +1

GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding

no code implementations20 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.

Image Inpainting Monocular Depth Estimation

Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias

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.

Data Augmentation Depth Estimation +1

Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation

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.

Depth Prediction Monocular Depth Estimation +2

Resisting Large Data Variations via Introspective Transformation Network

no code implementations16 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.

Data Augmentation Few-Shot Learning

Stretching Domain Adaptation: How far is too far?

no code implementations6 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.

Unsupervised Domain Adaptation

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