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Chengkan Lv

4 papers here

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.CV4
same name
  • Chengkan Lv — 1 paper

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 citedProgressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection

30 citations · 30 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2026

MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval

Chaoran Xu, Chengkan Lv, Qiyu Chen +2

Zero-shot anomaly detection (ZSAD) often leverages pretrained vision or vision-language models, but many existing methods use prompt learning or complex modeling to fit the data di…

cs.CV2025

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection

Qiyu Chen, Zhen Qu, Wei Luo +7

Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the mode…

cs.CV2025

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection

Qiyu Chen, Huiyuan Luo, Haiming Yao +4

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment cos…

cs.CV2024★ 30 cited

Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection

Qiyu Chen, Huiyuan Luo, Han Gao +2

Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of overfitting when learnin…

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