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researcher

Yingying Zhang

4 papers here

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.AI1
  • eess.IV1
ORCID 0000-0002-0964-1774
same name
  • Yingying Zhang — 10 papers, h 10
  • Yingying Zhang — 6 papers
  • Yingying Zhang — 4 papers
  • Yingying Zhang — 3 papers
  • Yingying Zhang — 3 papers, h 3
  • Yingying Zhang — 2 papers

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
20212025
most citedDivide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

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

collaborators

4 papers

cs.AI2025

LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners

Junhao Zheng, Xidi Cai, Qiuke Li +5

Lifelong learning is essential for intelligent agents operating in dynamic environments. Current large language model (LLM)-based agents, however, remain stateless and unable to ac…

eess.IV2025★ 2 cited

AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting

Xiaolin Fan, Yan Wang, Yingying Zhang +6

Automatic view positioning is crucial for cardiac computed tomography (CT) examinations, including disease diagnosis and surgical planning. However, it is highly challenging due to…

cs.CV2024

HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation

Xiaolong Wang, Lei Yu, Yingying Zhang +6

Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved…

cs.CV2021★ 2 cited

Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

Jinlei Hou, Yingying Zhang, Qiaoyong Zhong +3

Reconstruction-based methods play an important role in unsupervised anomaly detection in images. Ideally, we expect a perfect reconstruction for normal samples and poor reconstruct…

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