output
20052026
most citedRandom Erasing Data Augmentation

748 citations

Showing 2019 · cs.CVShow all

11 papers · 2 filters

cs.CV20198 cited

Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

Wenkai Han, Chenglu Wen, Cheng Wang +2

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent…

cs.CV20198 cited

A Real-time Global Inference Network for One-stage Referring Expression Comprehension

Yiyi Zhou, Rongrong Ji, Gen Luo +5

Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description. Most ex…

cs.CV201913 cited

Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

Fengxiang Yang, Ke Li, Zhun Zhong +7

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achi…

cs.CV20197 cited

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

Kekai Sheng, Weiming Dong, Menglei Chai +6

Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on…

cs.CV20192 cited

SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation

Sheng Jin, Shangchen Zhou, Yao Liu +4

Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting suffici…

cs.CV201915 cited

Fast Learning of Temporal Action Proposal via Dense Boundary Generator

Chuming Lin, Jian Li, Yabiao Wang +7

Generating temporal action proposals remains a very challenging problem, where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence…