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20162021
most citedSiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

139 citations · 294 across the 9 of their papers we have counts for

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17 papers · 1 filter

cs.CV20211 cited

Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection

Ajian Liu, Chenxu Zhao, Zitong Yu +11

Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from…

cs.CV202170 cited

Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking

Nan Jiang, Kuiran Wang, Xiaoke Peng +7

Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work,…

cs.CV2020

DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio

Jiangtao Kong, Yu Cheng, Benjia Zhou +2

Vehicle Re-identification (ReID) is an important yet challenging problem in computer vision. Compared to other visual objects like faces and persons, vehicles simultaneously exhibi…

cs.CV201916 cited

RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation

Shaoru Wang, Yongchao Gong, Junliang Xing +3

Object detection and instance segmentation are two fundamental computer vision tasks. They are closely correlated but their relations have not yet been fully explored in most previ…

cs.CV20195 cited

Relational Learning for Joint Head and Human Detection

Cheng Chi, Shifeng Zhang, Junliang Xing +3

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without consid…

cs.CV2019

PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

Cheng Chi, Shifeng Zhang, Junliang Xing +3

Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and effici…