activity
20182021
most citedLEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation

21 citations · 40 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2021

DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary

Linjie Wang, Quan Zhou, Chenfeng Jiang +2

Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the be…

cs.CV20212 cited

DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention

Huimin Shi, Quan Zhou, Yinghao Ni +2

Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices. To address the trade-off between…

cs.CV20219 cited

FPB: Feature Pyramid Branch for Person Re-Identification

Suofei Zhang, Zirui Yin, Xiofu Wu +3

High performance person Re-Identification (Re-ID) requires the model to focus on both global silhouette and local details of pedestrian. To extract such more representative feature…

cs.CV2020

BiCANet: Bi-directional Contextual Aggregating Network for Image Semantic Segmentation

Quan Zhou, Dechun Cong, Bin Kang +4

Exploring contextual information in convolution neural networks (CNNs) has gained substantial attention in recent years for semantic segmentation. This paper introduces a Bi-direct…

cs.CV20194 cited

FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation

Jia Liu, Quan Zhou, Yong Qiang +3

This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweight networ…

cs.CV20194 cited

ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation

Yu Wang, Quan Zhou, Xiaofu Wu

The recent years have witnessed great advances for semantic segmentation using deep convolutional neural networks (DCNNs). However, a large number of convolutional layers and featu…