English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Image anomaly detection (IAD) is an emerging and vital computer vision taskin industrial manufacturing (IM). Recently, many advanced algorithms have beenreported, but their performance deviates considerably with various IM settings.We realize that the lack of a uniform IM benchmark is hindering the developmentand usage of IAD methods in real-world applications. In addition, it isdifficult for researchers to analyze IAD algorithms without a uniformbenchmark. To solve this problem, we propose a uniform IM benchmark, for thefirst time, to assess how well these algorithms perform, which includes variouslevels of supervision (unsupervised versus fully supervised), learningparadigms (few-shot, continual and noisy label), and efficiency (memory usageand inference speed). Then, we construct a comprehensive image anomalydetection benchmark (IM-IAD), which includes 19 algorithms on seven majordatasets with a uniform setting. Extensive experiments (17,017 total) on IM-IADprovide in-depth insights into IAD algorithm redesign or selection. Moreover,the proposed IM-IAD benchmark challenges existing algorithms and suggestsfuture research directions. To foster reproducibility and accessibility, thesource code of IM-IAD is uploaded on the website,https://github.com/M-3LAB/IM-IAD.

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing