Technical Program

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SS-13: Explainable Machine Learning for Image Processing

Paper Presentations and Interactive Q&A Time: Wednesday, 28 October, 16:00 - 18:25
Virtual Session: View on Virtual Platform
Session Chairs: Arash Mohammadi, Concordia University and Yong Man Ro, Korea Advanced Institute of Science and Technology
 
 SS-13.1: SIDU: SIMILARITY DIFFERENCE AND UNIQUENESS METHOD FOR EXPLAINABLE AI
         Satya M. Muddamsetty; Aalborg University
         Mohammad N. S. Jahromi; Aalborg University
         Thomas B. Moeslund; Aalborg University
 
 SS-13.2: ROBUSTNESS AND OVERFITTING BEHAVIOR OF IMPLICIT BACKGROUND MODELS
         Shirley Liu; Georgia Institute of Technology
         Charles Lehman; Georgia Institute of Technology
         Ghassan AlRegib; Georgia Institute of Technology
 
 SS-13.3: ONLINE LEARNING FOR BETA-LIOUVILLE HIDDEN MARKOV MODELS: INCREMENTAL VARIATIONAL LEARNING FOR VIDEO SURVEILLANCE AND ACTION RECOGNITION
         Samr Ali; Concordia University
         Nizar Bouguila; Concordia University
 
 SS-13.4: TOWARDS HUMAN-LIKE INTERPRETABLE OBJECT DETECTION VIA SPATIAL RELATION ENCODING
         Jung Uk Kim; Korea Advanced Institute of Science and Technology (KAIST)
         Sungjune Park; Korea Advanced Institute of Science and Technology (KAIST)
         Yong Man Ro; Korea Advanced Institute of Science and Technology (KAIST)
 
 SS-13.5: CONTRASTIVE EXPLANATIONS IN NEURAL NETWORKS
         Mohit Prabhushankar; Georgia Institute of Technology
         Gukyeong Kwon; Georgia Institute of Technology
         Dogancan Temel; Georgia Institute of Technology
         Ghassan AlRegib; Georgia Institute of Technology
 
 SS-13.6: PIXELHOP++: A SMALL SUCCESSIVE-SUBSPACE-LEARNING-BASED (SSL-BASED) MODEL FOR IMAGE CLASSIFICATION
         Yueru Chen; University of Southern California
         Mozhdeh Rouhsedaghat; University of Southern California
         Suya You; Army Research Laboratory
         Raghuveer Rao; Army Research Laboratory
         C.-C. Jay Kuo; University of Southern California
 
 SS-13.7: DEEP-URL: A MODEL-AWARE APPROACH TO BLIND DECONVOLUTION BASED ON DEEP UNFOLDED RICHARDSON-LUCY NETWORK
         Chirag Agarwal; University of Illinois at Chicago
         Shahin Khobahi; University of Illinois at Chicago
         Arindam Bose; University of Illinois at Chicago
         Mojtaba Soltanalian; University of Illinois at Chicago
         Dan Schonfeld; University of Illinois at Chicago