Technical Program

GS-P.8: Neural networks for image and video processing

Symposium: General Symposium
Session Type: Poster
Time: Thursday, November 29, 15:50 - 17:20
Location: Sleeping Beauty Pavilion
 
Poster Board: 7
GS-P.8.7: CLASSIFICATION OF SEVERELY OCCLUDED IMAGE SEQUENCES VIA CONVOLUTIONAL RECURRENT NEURAL NETWORKS
         Jian Zheng; Binghamton University
         Yifan Wang; Binghamton University
         Xiaonan Zhang; Binghamton University
         Xiaohua Li; Binghamton University
 
Poster Board: 8
GS-P.8.8: RECONSTRUCTION-FREE DEEP CONVOLUTIONAL NEURAL NETWORKS FOR PARTIALLY OBSERVED IMAGES
         Arun Nair; Johns Hopkins University
         Luoluo Liu; Johns Hopkins University
         Akshay Rangamani; Johns Hopkins University
         Peter Chin; Johns Hopkins University
         Muyinatu A Lediju Bell; Johns Hopkins University
         Trac Tran; Johns Hopkins University
 
Poster Board: 9
GS-P.8.9: INTERACTIVE OBJECT SEGMENTATION WITH NOISY BINARY INPUTS
         Gregory Canal; Georgia Institute of Technology
         Sivabalan Manivasagam; Georgia Institute of Technology
         Shaoheng Liang; Tsinghua University
         Christopher Rozell; Georgia Institute of Technology
 
Poster Board: 10
GS-P.8.10: PERSON RE-IDENTIFICATION BY REFINED ATTRIBUTE PREDICTION AND WEIGHTED MULTI-PART CONSTRAINTS
         Xiao Hu; Beijing University of Posts and Telecommunications
         Xiaoqiang Guo; Academy of Broadcasting Science
         Zhuqing Jiang; Beijing University of Posts and Telecommunications
         Yun Zhou; Academy of Broadcasting Science
         Zixuan Yang; Beijing University of Posts and Telecommunications
 
Poster Board: 11
GS-P.8.11: REGION-PARTITION BASED BILINEAR FUSION NETWORK FOR PERSON RE-IDENTIFICATION
         Xiao Hu; Beijing University of Posts and Telecommunications
         Xiaoqiang Guo; Academy of Broadcasting Science
         Zhuqing Jiang; Beijing University of Posts and Telecommunications
         Yun Zhou; Academy of Broadcasting Science
         Zixuan Yang; Beijing University of Posts and Telecommunications