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Tianyi Zhao

4 papers hereh-index 9349 citations15 works total

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

author position
  • first author1
  • middle author1

Across the 2 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV1
same name
  • Tianyi Zhao — 6 papers, h 4
  • Tianyi Zhao — 6 papers, h 5
  • Tianyi Zhao — 4 papers, h 2
  • Tianyi Zhao — 1 paper, h 4
  • Tianyi Zhao — 1 paper, h 1
  • Tianyi Zhao — 1 paper, h 0

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
20172023
most citedEmbedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition

35 citations · 35 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2023

Learning to Pan-sharpening with Memories of Spatial Details

Maoxun Yuan, Tianyi Zhao, Bo Li +1

Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multispectral images (MS) to obt…

eess.IV2020

3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management

Tianyi Zhao, Kai Cao, Jiawen Yao +6

The pancreatic disease taxonomy includes ten types of masses (tumors or cysts)[20,8]. Previous work focuses on developing segmentation or classification methods only for certain ma…

cs.CV2017★ 35 cited

Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition

Tianyi Zhao, Baopeng Zhang, Wei Zhang +3

In this paper, a level-wise mixture model (LMM) is developed by embedding visual hierarchy with deep networks to support large-scale visual recognition (i.e., recognizing thousands…

cs.CV2017

Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

Tianyi Zhao, Jun Yu, Zhenzhong Kuang +2

In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task…

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