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researcher

Ming Shao

12 papers hereh-index 334.1k citations107 works total

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

author position
  • middle author12

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.LG2
  • cs.GR1
same name
  • Ming Shao — 3 papers, h 2
  • Ming Shao — 3 papers, h 3
  • Ming Shao — 3 papers, h 2
  • Ming Shao — 2 papers, h 3
  • Ming Shao — 1 paper, h 2
  • Ming Shao — 1 paper, h 1

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
20162021
most citedFamilies in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks

82 citations · 207 across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 13 cited

What Will Your Child Look Like? DNA-Net: Age and Gender Aware Kin Face Synthesizer

Pengyu Gao, Siyu Xia, Joseph Robinson +4

Visual kinship recognition aims to identify blood relatives from facial images. Its practical application-- like in law-enforcement, video surveillance, automatic family album mana…

cs.CV2019★ 5 cited

LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution

Bin Sun, Jun Li, Ming Shao +1

Deep learning has become popular in recent years primarily due to the powerful computing device such as GPUs. However, deploying these deep models to end-user devices, smart phones…

cs.CV2019★ 6 cited

Real-time Memory Efficient Large-pose Face Alignment via Deep Evolutionary Network

Bin Sun, Ming Shao, Siyu Xia +1

There is an urgent need to apply face alignment in a memory-efficient and real-time manner due to the recent explosion of face recognition applications. However, impact factors suc…

cs.LG2019

Learning Robust Data Representation: A Knowledge Flow Perspective

Zhengming Ding, Ming Shao, Handong Zhao +1

It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompletenes…

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