MA1.L3.4
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ATAC-NET: ZOOMED VIEW WORKS BETTER FOR ANOMALY DETECTION

Shaurya Gupta, Neil Gautam, Anurag Malyala, HyperVerge AI, India
Session:
MA1.L3: Explainable AI Methods Lecture
Track:
Visual Artificial Intelligence
Location:
Capital Suite - 16
Presentation Time:
Mon, 28 Oct, 09:24 - 09:42 Gulf Standard Time (UTC +4)
Session Chair:
Akka Zemmari, LaBRI, University of Bordeaux
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Session MA1.L3
MA1.L3.1: EXPLAINING REPRESENTATION LEARNING WITH PERCEPTUAL COMPONENTS
Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib, Georgia Institute of Technology, United States of America
MA1.L3.2: ET: EXPLAIN TO TRAIN: LEVERAGING EXPLANATIONS TO ENHANCE THE TRAINING OF A MULTIMODAL TRANSFORMER
Meghna P Ayyar, Jenny Benois-Pineau, Akka Zemmari, LaBRI, University of Bordeaux, France
MA1.L3.3: Saliency as a Schedule: Intuitive Image Attribution
Aniket Singh, Anoop Namboodiri, International Institute of Information Technology, Hyderabad, India
 Top 5%
MA1.L3.4: ATAC-NET: ZOOMED VIEW WORKS BETTER FOR ANOMALY DETECTION
Shaurya Gupta, Neil Gautam, Anurag Malyala, HyperVerge AI, India
MA1.L3.5: ROTATED R-CNN: A TWO-STAGE OBJECT DETECTION METHOD ADAPTED TO ORIENTED BOUNDING BOXES
Chengdao Pu, Jun Yu, University of Science and Technology of China, China; Wen Su, Zhejiang Sci-Tech University, China; Tianyu Liu, Jianghuai Advance Technology Center, China; ,
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