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researcher

Yan Wang

16 papers hereh-index 7244 citations18 works total

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

author position
  • middle author8
  • last author7

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • eess.IV7
  • cs.CR1
same name
  • Yan Wang — 24 papers, h 8
  • Yan Wang — 24 papers, h 25
  • Yan Wang — 23 papers, h 9
  • Yan Wang — 22 papers, h 3
  • Yan Wang — 21 papers, h 16
  • Yan Wang — 20 papers, h 12

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
20242026
most citedBoosting Neural Representations for Videos with a Conditional Decoder

2 citations · 2 across the 15 of their papers we have counts for

collaborators
Showing 2026 · cs.CVShow all

4 papers · 2 filters

cs.CV2026

Pixel-Space Diffusion via Observation Operators

Shaojie Guo, Lichen Ma, Haoyang Tong +8

Pixel-space diffusion models directly model image distributions but remain difficult to optimize. Recent methods alleviate this challenge through target reparameterization, while s…

cs.CV2026

MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction

Peize Li, Fanhu Zeng, Tongda Xu +5

In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can…

cs.CV2026

MeMix: Writing Less, Remembering More for Streaming 3D Reconstruction

Jiacheng Dong, Huan Li, Sicheng Zhou +3

Reconstruction is a fundamental task in 3D vision and a fundamental capability for spatial intelligence. Particularly, streaming 3D reconstruction is central to real-time spatial p…

cs.CV2026

OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution

Shijie Zhao, Xuanyu Zhang, Bin Chen +8

Aligning generative real-world image super-resolution models with human visual preference is challenging due to the perception--fidelity trade-off and diverse, unknown degradations…

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