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Hao Tang

34 papers hereh-index 222.6k citations39 works total

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

author position
  • first author9
  • middle author22
  • last author1

Across the 32 of 34 papers where every author was matched, so the position is known.

fields
  • cs.CV33
  • cs.IR1
same name
  • Hao Tang — 47 papers, h 29
  • Hao Tang — 41 papers, h 9
  • Hao Tang — 34 papers, h 19
  • Hao Tang — 31 papers, h 21
  • Hao Tang — 18 papers, h 7
  • Hao Tang — 15 papers, h 15

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
20192026
most citedImproving Semantic Segmentation of Aerial Images Using Patch-based Attention

414 citations · 1.1k across the 31 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023★ 8 cited

Interactive Neural Painting

Elia Peruzzo, Willi Menapace, Vidit Goel +9

In the last few years, Neural Painting (NP) techniques became capable of producing extremely realistic artworks. This paper advances the state of the art in this emerging research…

cs.CV2023

Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis

Hao Tang, Guolei Sun, Nicu Sebe +1

We propose a novel ECGAN for the challenging semantic image synthesis task. Although considerable improvements have been achieved by the community in the recent period, the quality…

cs.CV2023

Inter-Instance Similarity Modeling for Contrastive Learning

Chengchao Shen, Dawei Liu, Hao Tang +2

The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich inter-instance s…

cs.CV2023★ 1 cited

Graph Transformer GANs for Graph-Constrained House Generation

Hao Tang, Zhenyu Zhang, Humphrey Shi +5

We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained h…

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