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Ming-Ming Cheng

85 papers hereh-index 9156.1k citations307 works total

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

author position
  • first author1
  • middle author56
  • last author26

Across the 83 of 85 papers where every author was matched, so the position is known.

fields
  • cs.CV79
  • eess.IV2
  • cs.GR1
  • cs.LG1
  • cs.RO1
  • stat.ML1
same name
  • Ming-Ming Cheng — 28 papers, h 11
  • Ming-Ming Cheng — 15 papers, h 15
  • Ming-Ming Cheng — 15 papers, h 10
  • Ming-Ming Cheng — 11 papers, h 4
  • Ming-Ming Cheng — 10 papers, h 3
  • Ming-Ming Cheng — 8 papers, h 2

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
20132023
most citedRes2Net: A New Multi-scale Backbone Architecture

3.5k citations · 16k across the 85 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 2 cited

S4Net: Single Stage Salient-Instance Segmentation

Ruochen Fan, Ming-Ming Cheng, Qibin Hou +3

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments.…

cs.CV2017★ 6 cited

Image Matching: An Application-oriented Benchmark

JiaWang Bian, Le Zhang, Yun Liu +3

Image matching approaches have been widely used in computer vision applications in which the image-level matching performance of matchers is critical. However, it has not been well…

cs.CV2017★ 109 cited

Structure-measure: A New Way to Evaluate Foreground Maps

Deng-Ping Fan, Ming-Ming Cheng, Yun Liu +2

Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the filed of salient object detection where the purpose is to accu…

cs.CV2017★ 45 cited

Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

Yunchao Wei, Jiashi Feng, Xiaodan Liang +3

We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems. Cl…

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