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20152021
most citedRobust Visual Tracking Revisited: From Correlation Filter to Template Matching

67 citations · 92 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CV20211 cited

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Wei Liu, Pingping Zhang, Yinjie Lei +3

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among diff…

cs.CV2020

Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +4

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, t…

cs.CV20207 cited

Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis

Masoumeh Zareapoor, Pourya Shamsolmoali, Jie Yang

The collected data from industrial machines are often imbalanced, which poses a negative effect on learning algorithms. However, this problem becomes more challenging for a mixed t…

cs.CV2020

AMIL: Adversarial Multi Instance Learning for Human Pose Estimation

Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +1

Human pose estimation has an important impact on a wide range of applications from human-computer interface to surveillance and content-based video retrieval. For human pose estima…

cs.CV201967 cited

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

Fanghui Liu, Chen Gong, Xiaolin Huang +3

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs)…

cs.CV2019

A Regularization Approach for Instance-Based Superset Label Learning

Chen Gong, Tongliang Liu, Yuanyan Tang +3

Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training e…