Presentation

36. Classifying Texture Anomalies at First Sight
DescriptionThis poster summarizes our recent line of research on localization and classification of anomalies in real-world texture images. It presents our novel method for zero-shot anomaly localization (FCA), its extension to leverage contaminated data, and anomaly clustering through contrastive learning.
Event Type
Poster
TimeThursday, 1 August 20249:00am - 5:30pm MDT
LocationMile High Pre-Function Area
Session TimeSunday, 28 July 20249:00am - 5:30pm MDTMonday, 29 July 20249:00am - 5:30pm MDTTuesday, 30 July 20249:00am - 5:30pm MDTWednesday, 31 July 20249:00am - 5:30pm MDTThursday, 1 August 20249:00am - 5:30pm MDT
LocationMile High Pre-Function Area
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