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20182022
most citedFully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition

7 citations · 21 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV20226 cited

Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling

Xin Chen, Qingtao Tang, Ke Hu +4

User historical behaviors are proved useful for Click Through Rate (CTR) prediction in online advertising system. In Meituan, one of the largest e-commerce platform in China, an it…

cs.CV20211 cited

EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation

Yanjun Gao, Lulu Liu, Jason Wang +3

Temporal grounding aims to predict a time interval of a video clip corresponding to a natural language query input. In this work, we present EVOQUER, a temporal grounding framework…

cs.CV2019

Attention Distillation for Learning Video Representations

Miao Liu, Xin Chen, Yun Zhang +2

We address the challenging problem of learning motion representations using deep models for video recognition. To this end, we make use of attention modules that learn to highlight…

cs.CV2018

Unsupervised Domain Adaptation using Generative Models and Self-ensembling

Eman T. Hassan, Xin Chen, David Crandall

Transferring knowledge across different datasets is an important approach to successfully train deep models with a small-scale target dataset or when few labeled instances are avai…

cs.CV20187 cited

Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition

Mingze Xu, Aidean Sharghi, Xin Chen +1

A major emerging challenge is how to protect people's privacy as cameras and computer vision are increasingly integrated into our daily lives, including in smart devices inside hom…