All times are in Alaska local time (AKDT/UTC−08:00)
MLR-APPL-IP-3: Machine learning for image processing 3 |
Interactive Q&A Time: Tuesday, September 21, 08:00 - 09:30 |
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 |