activity
20182022
most citedGroup Fisher Pruning for Practical Network Compression

25 citations · 28 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20223 cited

Group R-CNN for Weakly Semi-supervised Object Detection with Points

Shilong Zhang, Zhuoran Yu, Liyang Liu +3

We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding bo…

cs.CV2021

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Yi Li, Zhanghui Kuang, Liyang Liu +2

Most weakly supervised semantic segmentation (WSSS) methods follow the pipeline that generates pseudo-masks initially and trains the segmentation model with the pseudo-masks in ful…

cs.CV202125 cited

Group Fisher Pruning for Practical Network Compression

Liyang Liu, Shilong Zhang, Zhanghui Kuang +7

Network compression has been widely studied since it is able to reduce the memory and computation cost during inference. However, previous methods seldom deal with complicated stru…

cs.RO2020

Active and Interactive Mapping with Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators

Liyang Liu, Simon Fryc, Lan Wu +3

In this letter, we present an interactive probabilistic mapping framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where t…

cs.RO2020

Faithful Euclidean Distance Field from Log-Gaussian Process Implicit Surfaces

Lan Wu, Ki Myung Brian Lee, Liyang Liu +1

In this letter, we introduce the Log-Gaussian Process Implicit Surface (Log-GPIS), a novel continuous and probabilistic mapping representation suitable for surface reconstruction a…

cs.RO2018

Parallax Bundle Adjustment on Manifold with Convexified Initialization

Liyang Liu, Teng Zhang, Yi Liu +4

Bundle adjustment (BA) with parallax angle based feature parameterization has been shown to have superior performance over BA using inverse depth or XYZ feature forms. In this pape…