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Ming-Ming Cheng

77 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
  • middle author46
  • last author25

Across the 71 of 77 papers where every author was matched, so the position is known.

fields
  • cs.CV72
  • eess.IV2
  • 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 — 9 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
20162023
most citedDense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

348 citations · 964 across the 24 of their papers we have counts for

collaborators
Showing 2017 · cs.CVShow all

4 papers · 2 filters

cs.CV2017

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

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

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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