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Weijia Wu

5 papers hereh-index 5119 citations7 works total

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

author position
  • middle author5

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

fields
  • cs.CV4
  • cs.CL1
same name
  • Weijia Wu — 20 papers, h 12
  • Weijia Wu — 11 papers, h 4
  • Weijia Wu — 4 papers, h 4
  • Weijia Wu — 4 papers, h 3
  • Weijia Wu — 3 papers, h 1
  • Weijia Wu — 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
20192024
most citedExplore Faster Localization Learning For Scene Text Detection

2 citations · 4 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024★ 1 cited

ControlCap: Controllable Region-level Captioning

Yuzhong Zhao, Yue Liu, Zonghao Guo +4

Region-level captioning is challenged by the caption degeneration issue, which refers to that pre-trained multimodal models tend to predict the most frequent captions but miss the…

cs.CV2023

Generative Prompt Model for Weakly Supervised Object Localization

Yuzhong Zhao, Qixiang Ye, Weijia Wu +2

Weakly supervised object localization (WSOL) remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively tra…

cs.CV2023

FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation

Yuzhong Zhao, Weijia Wu, Zhuang Li +2

Current video text spotting methods can achieve preferable performance, powered with sufficient labeled training data. However, labeling data manually is time-consuming and labor-i…

cs.CV2022★ 2 cited

Explore Faster Localization Learning For Scene Text Detection

Yuzhong Zhao, Yuanqiang Cai, Weijia Wu +1

Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks. In this paper, we present a new scene…

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