activity
20182021
most citedCrowd Counting and Density Estimation by Trellis Encoder-Decoder Network

78 citations · 110 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV20212 cited

SwiftNet: Real-time Video Object Segmentation

Haochen Wang, Xiaolong Jiang, Haibing Ren +2

In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J &F and 70 FPS on DAVIS 2017 validation dataset, leadin…

cs.CV2021

Horizontal-to-Vertical Video Conversion

Tun Zhu, Daoxin Zhang, Yao Hu +4

Alongside the prevalence of mobile videos, the general public leans towards consuming vertical videos on hand-held devices. To revitalize the exposure of horizontal contents, we he…

cs.CV2020

NAS-Count: Counting-by-Density with Neural Architecture Search

Yutao Hu, Xiaolong Jiang, Xuhui Liu +4

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation p…

cs.CV2019

Bayesian Optimized 1-Bit CNNs

Jiaxin Gu, Junhe Zhao, Xiaolong Jiang +4

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great cha…

cs.CV201927 cited

Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking

Xiaolong Jiang, Peizhao Li, Yanjing Li +1

In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT). Given detection responses, we formulate the frame-by-frame dat…

cs.CV201978 cited

Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network

Xiaolong Jiang, Zehao Xiao, Baochang Zhang +4

Crowd counting has recently attracted increasing interest in computer vision but remains a challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet)…