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MLR-APPL-IP-3: Machine learning for image processing 3

Session Type: Poster
Time: Tuesday, September 21, 08:00 - 09:30
Location: Area F
Session Chair: Xin Ding, UBC
 
   MLR-APPL-IP-3.1: HIERARCHICAL REGION PROPOSAL REFINEMENT NETWORK FOR WEAKLY SUPERVISED OBJECT DETECTION
         Ming Zhang; University of Electronic Science and Technology of China
         Shuaicheng Liu; University of Electronic Science and Technology of China
         Bing Zeng; University of Electronic Science and Technology of China
 
   MLR-APPL-IP-3.2: DEEP SENSOR FUSION BASED ON FRUSTUM POINT SINGLE SHOT MULTIBOX DETECTOR FOR 3D OBJECT DETECTION
         Yu Wang; Harbin Institute of Technology
         Ye Zhang; Harbin Institute of Technology
         Shaohua Zhai; Harbin Institute of Technology
         Hao Chen; Harbin Institute of Technology
         Shaoqi Shi; Harbin Institute of Technology
         Gang Wang; Alibaba Group
 
   MLR-APPL-IP-3.3: MULTI-SCALE GRAPH CONVOLUTIONAL INTERACTION NETWORK FOR SALIENT OBJECT DETECTION
         Wenqi Che; Shanghai University
         Luoyi Sun; Shanghai University
         Zhifeng Xie; Shanghai University
         Youdong Ding; Shanghai University
         Kaili Han; Shanghai University
 
   MLR-APPL-IP-3.4: MULTISCALE IOU: A METRIC FOR EVALUATION OF SALIENT OBJECT DETECTION WITH FINE STRUCTURES
         Azim Ahmadzadeh; Georgia State University
         Dustin J. Kempton; Georgia State University
         Yang Chen; Georgia State University
         Rafal A. Angryk; Georgia State University
 
   MLR-APPL-IP-3.5: SGE NET: VIDEO OBJECT DETECTION WITH SQUEEZED GRU AND INFORMATION ENTROPY MAP
         Rui Su; Waseda University
         Wenjing Huang; Waseda University
         Haoyu Ma; University of California, Irvine
         Xiaowei Song; Southeast University
         Jinglu Hu; Waseda University
 
   MLR-APPL-IP-3.6: PROVABLE TRANSLATIONAL ROBUSTNESS FOR OBJECT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS
         Axel Vierling; Technische Universität Kaiserslautern
         Charu James; Technische Universität Kaiserslautern
         Nikoletta Katsaouni; Goethe University Frankfurt am Main
         Karsten Berns; Technische Universität Kaiserslautern
 
   MLR-APPL-IP-3.7: EFFECTIVE FEATURE FUSION NETWORK IN BIFPN FOR SMALL OBJECT DETECTION
         Jun Chen; China University of Geosciences, Wuhan
         HongSheng Mai; China University of Geosciences, Wuhan
         Linbo Luo; China University of Geosciences, Wuhan
         Xiaoqiang Chen; China University of Geosciences, Wuhan
         Kangle Wu; China University of Geosciences, Wuhan
 
   MLR-APPL-IP-3.8: OBJECT DETECTION AND AUTOENCODER-BASED 6D POSE ESTIMATION FOR HIGHLY CLUTTERED BIN PICKING
         Timon Höfer; University of Tuebingen
         Faranak Shamsafar; University of Tuebingen
         Nuri Benbarka; University of Tuebingen
         Andreas Zell; University of Tuebingen
 
   MLR-APPL-IP-3.9: HUMAN VISION-LIKE ROBUST OBJECT RECOGNITION
         Peng Kang; Northwestern University
         Hao Hu; University of British Columbia
         Srutarshi Banerjee; Northwestern University
         Henry Chopp; Northwestern University
         Aggelos K. Katsaggelos; Northwestern University
         Oliver Cossairt; Northwestern University
 
   MLR-APPL-IP-3.10: TRAINING AN EMBEDDED OBJECT DETECTOR FOR INDUSTRIAL SETTINGS WITHOUT REAL IMAGES
         Julia Cohen; Université Lyon 2 - LIRIS (CNRS)
         Carlos Crispim-Junior; Université Lyon 2 - LIRIS (CNRS)
         Jean-Marc Chiappa; DEMS
         Laure Tougne; Université Lyon 2 - LIRIS (CNRS)
 
   MLR-APPL-IP-3.11: SEMI-SUPERVISED OBJECT DETECTION WITH SPARSELY ANNOTATED DATASET
         Jihun Yoon; hutom
         Seungbum Hong; hutom
         Min-Kook Choi; hutom
 
   MLR-APPL-IP-3.12: PSEUDO-LABEL GENERATION-EVALUATION FRAMEWORK FOR CROSS DOMAIN WEAKLY SUPERVISED OBJECT DETECTION
         Shengxiong Ouyang; Zhejiang University
         Xinglu Wang; Zhejiang University
         Kejie Lyu; Zhejiang University
         Yingming Li; Zhejiang University
 
   MLR-APPL-IP-3.13: BOTTOM-UP SALIENCY MEETS TOP-DOWN SEMANTICS FOR OBJECT DETECTION
         Tomoya Sawada; Mitsubishi Electric Corporation
         Teng-Yok Lee; Mitsubishi Electric Corporation
         Masahiro Mizuno; Mitsubishi Electric Corporation