activity
20182020
most citedDrone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

27 citations · 50 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CV2020

Multi-Drone based Single Object Tracking with Agent Sharing Network

Pengfei Zhu, Jiayu Zheng, Dawei Du +3

Drone equipped with cameras can dynamically track the target in the air from a broader view compared with static cameras or moving sensors over the ground. However, it is still cha…

cs.CV2020

Spatial Attention Pyramid Network for Unsupervised Domain Adaptation

Congcong Li, Dawei Du, Libo Zhang +4

Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, and semantic segmentation, which aims to alleviate per…

cs.CV20209 cited

SiamMan: Siamese Motion-aware Network for Visual Tracking

Wenzhang Zhou, Longyin Wen, Libo Zhang +3

In this paper, we present a novel siamese motion-aware network (SiamMan) for visual tracking, which consists of the siamese feature extraction subnetwork, followed by the classific…

cs.CV20193 cited

Learning Semantic Neural Tree for Human Parsing

Ruyi Ji, Dawei Du, Libo Zhang +5

The majority of existing human parsing methods formulate the task as semantic segmentation, which regard each semantic category equally and fail to exploit the intrinsic physiologi…

cs.CV201927 cited

Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

Longyin Wen, Dawei Du, Pengfei Zhu +4

This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones…

cs.CV201911 cited

Guided Attention Network for Object Detection and Counting on Drones

Yuanqiang Cai, Dawei Du, Libo Zhang +4

Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new G…