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researcher

Wei Wu

5 papers hereh-index 6201 citations8 works total

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

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.RO2
same name
  • Wei Wu — 14 papers, h 5
  • Wei Wu — 13 papers, h 7
  • Wei Wu — 8 papers, h 6
  • Wei Wu — 6 papers, h 1
  • Wei Wu — 5 papers, h 2
  • Wei Wu — 5 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

collaborators

5 papers

cs.RO2026

From Foundation to Application: Improving VLA Models in Practice

Wei Wu, Fangjing Wang, Fan Lu +21

Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bri…

cs.RO2026

A Pragmatic VLA Foundation Model

Wei Wu, Fan Lu, Yunnan Wang +22

Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensu…

cs.CV2026

Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning

Enhan Zhao, Wei Wu, Yuanrui Zhang +2

Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs). Existing approaches largely rely on large-scale, statically curated datasets, where a…

cs.CV2025

Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning

Fan Lu, Wei Wu, Kecheng Zheng +7

Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have dev…

cs.CV2025

Learning Visual Generative Priors without Text

Shuailei Ma, Kecheng Zheng, Ying Wei +7

Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that gra…

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