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20152021
most citedVisual Relationship Detection with Relative Location Mining

15 citations · 31 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV20211 cited

Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection

Yan Luo, Chongyang Zhang, Muming Zhao +2

Accurate pedestrian classification and localization have received considerable attention due to their wide applications such as security monitoring, autonomous driving, etc. Althou…

cs.CV2021

Embracing Uncertainty: Decoupling and De-bias for Robust Temporal Grounding

Hao Zhou, Chongyang Zhang, Yan Luo +2

Temporal grounding aims to localize temporal boundaries within untrimmed videos by language queries, but it faces the challenge of two types of inevitable human uncertainties: quer…

cs.CV202014 cited

Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection

Yan Luo, Chongyang Zhang, Muming Zhao +2

Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and…

cs.CV201915 cited

Visual Relationship Detection with Relative Location Mining

Hao Zhou, Chongyang Zhang, Chuanping Hu

Visual relationship detection, as a challenging task used to find and distinguish the interactions between object pairs in one image, has received much attention recently. In this…

cs.CV2019

Rethinking Classification and Localization for Cascade R-CNN

Ang Li, Xue Yang, Chongyang Zhang

We extend the state-of-the-art Cascade R-CNN with a simple feature sharing mechanism. Our approach focuses on the performance increases on high IoU but decreases on low IoU thresho…

cs.CV20191 cited

Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks

Muming Zhao, Jian Zhang, Chongyang Zhang +1

High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the obj…