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Ming Wu

4 papers hereh-index 192.4k citations83 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.CV4
same name
  • Ming Wu — 2 papers
  • Ming Wu — 2 papers
  • Ming Wu — 1 paper, h 3
  • Ming Wu — 1 paper
  • Ming Wu — 1 paper, h 21
  • Ming Wu — 1 paper

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

most citedGINet: Graph Interaction Network for Scene Parsing

1 citations · 1 across the 1 of their papers we have counts for

collaborators

4 papers

cs.CV2020★ 1 cited

GINet: Graph Interaction Network for Scene Parsing

Tianyi Wu, Yu Lu, Yu Zhu +4

Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorporate the linguistic kno…

cs.CV2020

FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images

Kaiyan Chen, Ming Wu, Jiaming Liu +1

Ship detection using high-resolution remote sensing images is an important task, which contribute to sea surface regulation. The complex background and special visual angle make sh…

cs.CV2020

Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

Yifeng Ding, Shaoguo Wen, Jiyang Xie +4

Classifying the sub-categories of an object from the same super-category (e.g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on…

cs.CV2020

C-DLinkNet: considering multi-level semantic features for human parsing

Yu Lu, Muyan Feng, Ming Wu +1

Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human. The challenge of human…

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