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Tengfei Wang

HKUST

14 papers hereh-index 132.5k citations15 works total

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

author position
  • first author5
  • middle author9

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV14
affiliations
  • HKUST
Homepage
same name
  • Tengfei Wang — 13 papers, h 6
  • Tengfei Wang — 7 papers, h 7
  • Tengfei Wang — 7 papers, h 4
  • Tengfei Wang — 3 papers, h 5
  • Tengfei Wang — 2 papers
  • Tengfei Wang — 2 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
20212026
most citedPretraining is All You Need for Image-to-Image Translation

92 citations · 140 across the 14 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021

Dual-Camera Super-Resolution with Aligned Attention Modules

Tengfei Wang, Jiaxin Xie, Wenxiu Sun +2

We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and…

cs.CV2021★ 18 cited

High-Fidelity GAN Inversion for Image Attribute Editing

Tengfei Wang, Yong Zhang, Yanbo Fan +2

We present a novel high-fidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved (e.g., backgroun…

cs.CV2021★ 3 cited

Internal Video Inpainting by Implicit Long-range Propagation

Hao Ouyang, Tengfei Wang, Qifeng Chen

We propose a novel framework for video inpainting by adopting an internal learning strategy. Unlike previous methods that use optical flow for cross-frame context propagation to in…

cs.CV2021★ 4 cited

Image Inpainting with External-internal Learning and Monochromic Bottleneck

Tengfei Wang, Hao Ouyang, Qifeng Chen

Although recent inpainting approaches have demonstrated significant improvements with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt col…

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