Search Results for author: Peidong Liu

Found 10 papers, 5 papers with code

WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations

no code implementations7 Jul 2021 Peidong Liu, Zibin He, Xiyu Yan, Yong Jiang, Shutao Xia, Feng Zheng, Maowei Hu

In this work, we propose an effective weakly-supervised video semantic segmentation pipeline with click annotations, called WeClick, for saving laborious annotating effort by segmenting an instance of the semantic class with only a single click.

Knowledge Distillation Model Compression +2

Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search

1 code implementation ICLR 2021 Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang, Yong Jiang, Zhenguo Li

For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.

Model Optimization object-detection +1

Deep Shutter Unrolling Network

1 code implementation CVPR 2020 Peidong Liu, Zhaopeng Cui, Viktor Larsson, Marc Pollefeys

The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network.

Motion Estimation

Self-Supervised Linear Motion Deblurring

1 code implementation10 Feb 2020 Peidong Liu, Joel Janai, Marc Pollefeys, Torsten Sattler, Andreas Geiger

Motion blurry images challenge many computer vision algorithms, e. g, feature detection, motion estimation, or object recognition.

Deblurring Image Deblurring +3

Visual Privacy Protection via Mapping Distortion

1 code implementation5 Nov 2019 Yiming Li, Peidong Liu, Yong Jiang, Shu-Tao Xia

To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios.

Deep Flow Collaborative Network for Online Visual Tracking

no code implementations5 Nov 2019 Peidong Liu, Xiyu Yan, Yong Jiang, Shu-Tao Xia

The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network.

Optical Flow Estimation Visual Tracking

Robust Dense Mapping for Large-Scale Dynamic Environments

no code implementations7 May 2019 Ioan Andrei Bârsan, Peidong Liu, Marc Pollefeys, Andreas Geiger

We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars.

Semantic Segmentation Visual Odometry

Efficient 2D-3D Matching for Multi-Camera Visual Localization

no code implementations17 Sep 2018 Marcel Geppert, Peidong Liu, Zhaopeng Cui, Marc Pollefeys, Torsten Sattler

This results in a system that provides reliable and drift-less pose estimations for high speed autonomous driving.


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