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20182021
most citedUser-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning

9 citations · 10 across the 4 of their papers we have counts for

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8 papers · 1 filter

cs.CV20219 cited

User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning

Pei Lv, Jianqi Fan, Xixi Nie +5

Personalized image aesthetic assessment (PIAA) has recently become a hot topic due to its usefulness in a wide variety of applications such as photography, film and television, e-c…

cs.CV2021

Probability Trajectory: One New Movement Description for Trajectory Prediction

Pei Lv, Hui Wei, Tianxin Gu +4

Trajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Currently, most of existing work treat the…

cs.CV20191 cited

Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization

Pei Lv, Haiyu Yu, Junxiao Xue +5

Localizing objects with weak supervision in an image is a key problem of the research in computer vision community. Many existing Weakly-Supervised Object Localization (WSOL) appro…

cs.CV2018

Abnormal Event Detection and Location for Dense Crowds using Repulsive Forces and Sparse Reconstruction

Pei Lv, Shunhua Liu, Mingliang Xu +1

This paper proposes a method based on repulsive forces and sparse reconstruction for the detection and location of abnormal events in crowded scenes. In order to avoid the challeng…

cs.CV2018

MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects

Lisha Cui, Rui Ma, Pei Lv +4

For most of the object detectors based on multi-scale feature maps, the shallow layers are rich in fine spatial information and thus mainly responsible for small object detection.…

cs.CV2018

USAR: an Interactive User-specific Aesthetic Ranking Framework for Images

Pei Lv, Meng Wang, Yongbo Xu +5

When assessing whether an image is of high or low quality, it is indispensable to take personal preference into account. Existing aesthetic models lay emphasis on hand-crafted feat…