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Yi Zhu

27 papers hereh-index 194k citations39 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author17
  • middle author7
  • last author1

Across the 26 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • cs.CR1
  • cs.LG1
same name
  • Yi Zhu — 45 papers, h 29
  • Yi Zhu — 21 papers, h 14
  • Yi Zhu — 18 papers, h 8
  • Yi Zhu — 17 papers, h 16
  • Yi Zhu — 14 papers, h 14
  • Yi Zhu — 14 papers, h 8

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162026
most citedMotion-Aware Feature for Improved Video Anomaly Detection

116 citations · 176 across the 11 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019★ 116 cited

Motion-Aware Feature for Improved Video Anomaly Detection

Yi Zhu, Shawn Newsam

Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feat…

cs.LG2019

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Jian Guo, He He, Tong He +13

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…

cs.CV2019

Exploring Temporal Information for Improved Video Understanding

Yi Zhu

In this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and s…

cs.CV2019★ 6 cited

Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery

Xueqing Deng, Yi Zhu, Shawn Newsam

This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.