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researcher

Rui Wang

4 papers hereh-index 5837 citations7 works total

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

author position
  • middle author4

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

fields
  • cs.CV3
  • cs.LG1
same name
  • Rui Wang — 44 papers, h 33
  • Rui Wang — 34 papers, h 13
  • Rui Wang — 34 papers, h 15
  • Rui Wang — 31 papers, h 26
  • Rui Wang — 27 papers, h 32
  • Rui Wang — 26 papers, h 13

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
20222025
most citedNext-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

138 citations · 141 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2025★ 1 cited

Seedance 1.0: Exploring the Boundaries of Video Generation Models

Yu Gao, Haoyuan Guo, Tuyen Hoang +41

Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously b…

cs.CV2025★ 1 cited

Seed1.5-VL Technical Report

Dong Guo, Faming Wu, Feida Zhu +194

We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…

cs.LG2024★ 1 cited

AffineQuant: Affine Transformation Quantization for Large Language Models

Yuexiao Ma, Huixia Li, Xiawu Zheng +6

The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing…

cs.CV2022★ 138 cited

Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Jiashi Li, Xin Xia, Wei Li +6

Due to the complex attention mechanisms and model design, most existing vision Transformers (ViTs) can not perform as efficiently as convolutional neural networks (CNNs) in realist…

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