Technical Program

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ARS-12: Machine Learning for Image and Video Classification II

Interactive Q&A Time: Tuesday, 27 October, 16:00 - 16:25
Virtual Session: View on Virtual Platform
Session Chair: Jingjing Meng, The State University of New York at Buffalo
 
 ARS-12.1: DEEP ADVERSARIAL ACTIVE LEARNING WITH MODEL UNCERTAINTY FOR IMAGE CLASSIFICATION
         Zheng Zhu; ChongQing University
         Hongxing Wang; ChongQing University
 
 ARS-12.2: COLLABORATIVE LEARNING OF SEMI-SUPERVISED CLUSTERING AND CLASSIFICATION FOR LABELING UNCURATED DATA
         Sara Mousavi; University of Tennessee, Knoxville
         Dylan Lee; University of Tennessee, Knoxville
         Tatianna Griffin; University of Tennessee, Knoxville
         Dawnie Steadman; University of Tennessee, Knoxville
         Audris Mockus; University of Tennessee, Knoxville
 
 ARS-12.3: M-SOSANET : AN EFFICIENT CONVOLUTION NETWORK BACKBONE FOR EMBEDDING DEVICES
         Tangkun Zhang; Beijing University of Posts and Telecommunications
         Jichao Jiao; Beijing University of Posts and Telecommunications
         Chengkai Zhang; Beijing University of Posts and Telecommunications
         Yaxin Zhao; Beijing University of Posts and Telecommunications
         Chenxu Wang; Beijing University of Posts and Telecommunications
         Wei Cui; Beijing University of Posts and Telecommunications
         Xinping Chen; Beijing University of Posts and Telecommunications
 
 ARS-12.4: EMOTION TRANSFORMATION FEATURE: NOVEL FEATURE FOR DECEPTION DETECTION IN VIDEOS
         Jun-Teng Yang; National Tsing Hua University
         Guei-Ming Liu; National Tsing Hua University
         Scott C.-H Huang; National Tsing Hua University
 
 ARS-12.5: UNKNOWN CLASS LABEL CLEANING FOR LEARNING WITH OPEN-SET NOISY LABELS
         Qing Yu; University of Tokyo
         Kiyoharu Aizawa; University of Tokyo
 
 ARS-12.6: CONTINUAL LOCAL TRAINING FOR BETTER INITIALIZATION OF FEDERATED MODELS
         Xin Yao; Tsinghua University
         Lifeng Sun; Tsinghua University
 
 ARS-12.7: CASCADED CONTEXT DEPENDENCY: AN EXTREMELY LIGHTWEIGHT MODULE FOR DEEP CONVOLUTIONAL NEURAL NETWORKS
         Xu Ma; University of North Texas
         Zhinan Qiao; University of North Texas
         Jingda Guo; University of North Texas
         Sihai Tang; University of North Texas
         Qi Chen; University of North Texas
         Qing Yang; University of North Texas
         Song Fu; University of North Texas
 
 ARS-12.8: SCW-SGD: STOCHASTICALLY CONFIDENCE-WEIGHTED SGD
         Takumi Kobayashi; National Institute of Advanced Industrial Science and Technology (AIST)
 
 ARS-12.9: CHANNEL PRUNING VIA GRADIENT OF MUTUAL INFORMATION FOR LIGHT-WEIGHT CONVOLUTIONAL NEURAL NETWORKS
         Min Kyu Lee; Inha University
         Seung Hyeon Lee; Inha University
         Sang Hyuk Lee; Inha University
         Byung Cheol Song; Inha University
 
 ARS-12.10: GOING DEEPER WITH NEURAL NETWORKS WITHOUT SKIP CONNECTIONS
         Oyebade Oyedotun; University of Luxembourg
         Abdelrahman Shabayek; University of Luxembourg
         Djamila Aouada; University of Luxembourg
         Bjorn Ottersten; University of Luxembourg
 
 ARS-12.11: ATTENTION BOOSTED DEEP NETWORKS FOR VIDEO CLASSIFICATION
         Junyong You; Norwegian Research Centre (NORCE)
         Jari Korhonen; Shenzhen University
 
 ARS-12.12: SKETCHED SPARSE SUBSPACE CLUSTERING FOR LARGE-SCALE HYPERSPECTRAL IMAGES
         Shaoguang Huang; Ghent University
         Hongyan Zhang; Wuhan University
         Aleksandra Pizurica; Ghent University
 
 ARS-12.13: INCREMENTAL FAST SUBCLASS DISCRIMINANT ANALYSIS
         Kateryna Chumachenko; Tampere University
         Jenni Raitoharju; Tampere University
         Moncef Gabbouj; Tampere University
         Alexandros Iosifidis; Aarhus University
 
 ARS-12.14: FEATURE COMPARISON BASED CHANNEL ATTENTION FOR FINE-GRAINED VISUAL CLASSIFICATION
         Shukun Jia; CRISE, Institute of Automation, Chinese Academy of Sciences
         Yan Bai; CRISE, Institute of Automation, Chinese Academy of Sciences
         Jing Zhang; CRISE, Institute of Automation, Chinese Academy of Sciences