activity
20192021
most citedLSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

91 citations · 117 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV20217 cited

SiamCorners: Siamese Corner Networks for Visual Tracking

Kai Yang, Zhenyu He, Wenjie Pei +4

The current Siamese network based on region proposal network (RPN) has attracted great attention in visual tracking due to its excellent accuracy and high efficiency. However, the…

eess.IV2020

FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement

Xudong Zhang, Bo Wang, Di Yuan +2

Automatic pneumonia Detection based on deep learning has increasing clinical value. Although the existing Feature Pyramid Network (FPN) and its variants have already achieved some…

eess.IV2020

Efficient Medical Image Segmentation with Intermediate Supervision Mechanism

Di Yuan, Junyang Chen, Zhenghua Xu +3

Because the expansion path of U-Net may ignore the characteristics of small targets, intermediate supervision mechanism is proposed. The original mask is also entered into the netw…

cs.NI2020

Robot Trajectory Planning With QoS Constrained IRS-assisted Millimeter-Wave Communications

Cristian Tatino, Nikolaos Pappas, Di Yuan

This paper considers the joint optimization of trajectory and beamforming of a wirelessly connected robot using intelligent reflective surface (IRS)-assisted millimeter-wave (mm-wa…

cs.CV202091 cited

LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

Qiao Liu, Xin Li, Zhenyu He +8

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a…

cs.CV20198 cited

Multi-Task Driven Feature Models for Thermal Infrared Tracking

Qiao Liu, Xin Li, Zhenyu He +4

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither eff…