Awards

Best Paper Award and Best Student Paper Award

These awards are intended to recognize exceptional contributions and encourage new entrants into the fields within the technical scope of the conference. Winners will be selected using paper reviews and additional study by an Awards Committee. Two papers will be selected for the Best Paper Award and three for the Best Student Paper Award. Winners receive a cash prize (split among authors for the Best Paper Award and among student authors for the Best Student Paper Award) and a certificate.  The award will be presented during IEEE ICIP 2019, and also announced on the conference website.

Best Paper and Best Student Paper* Finalists

Session

Title

TP.L8.1

IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS 

 

Diego Valsesia; Politecnico di Torino         

Giulia Fracastoro; Politecnico di Torino         

Enrico Magli; Politecnico di Torino

TA.L5.1

LEARNING TO RENDER BETTER IMAGE PREVIEWS

 

Shumeet Baluja; Google Inc.         

David Marwood; Google Inc.         

Nick Johnston; Google Inc.         

Michele Covell; Google Inc.

WQ.PB.6

EXTENDING LBP AND CONVOLUTION-LIKE OPERATIONS ON THE MESH*

 

Claudio Tortorici; Khalifa University
Naoufel Werghi; Khalifa University

Stefano Berretti; University of Florence  

WA.L1.4

ADAPTIVE INFERENCE USING HIERARCHICAL CONVOLUTIONAL BAG-OF-FEATURES FOR LOW-POWER EMBEDDED PLATFORMS

 

Nikolaos Passalis; Tampere University

Jenni Raitoharju; Tampere University

Anastasios Tefas; Aristotle University of Thessaloniki

Moncef Gabbouj; Tampere University

MA.PA.4

CONTINUOUS SIGN LANGUAGE RECOGNITION VIA REINFORCEMENT LEARNING*

 

Zhihao Zhang; University of Science and Technology of China
Junfu Pu; University of Science and Technology of China
Liansheng Zhuang; University of Science and Technology of China
Wengang Zhou; University of Science and Technology of China
Houqiang Li; University of Science and Technology of China

TA.L9.5

BLAST-NET: SEMANTIC SEGMENTATION OF HUMAN BLASTOCYST COMPONENTS VIA CASCADED ATROUS PYRAMID AND DENSE PROGRESSIVE UPSAMPLING*

 

Reza Moradi Rad; Simon Fraser University

Parvaneh Saeedi; Simon Fraser University

Jason Au; Pacific Centre for Reproductive Medicine

Jon Havelock; Pacific Centre for Reproductive Medicine

TA.PH.2

UPDCNN: A NEW SCHEME FOR IMAGE UPSAMPLING AND DEBLURRING USING A DEEP CONVOLUTIONAL NEURAL NETWORK*

 

Alireza Esmaeilzehi; Concordia University

M. Omair Ahmad; Concordia University

M.N.S. Swamy; Concordia University

MA.L6.2

UNCONSTRAINED FLOOD EVENT DETECTION USING ADVERSARIAL DATA AUGMENTATION*

 

Samira Pouyanfar; Florida International University

Yudong Tao; University of Miami

Saad Sadiq; University of Miami

Haiman Tian; Florida International University

Yuexuan Tu; University of Miami

Tianyi Wang; Florida International University

Shu-Ching Chen; Florida International University

Mei-Ling Shyu; University of Miami

WA.L7.1

DETECTING GENERATED IMAGE BASED ON A COUPLED NETWORK WITH TWO-STEP PAIRWISE LEARNING*

 

Yi-Xiu Zhuang; National Pingtung University Science and Technology

Chih-Chung Hsu; National Pingtung University Science and Technology

TQ.PE.4

HIGH JOINT SPECTRAL-SPATIAL RESOLUTION IMAGING VIA NANOSTRUCTURED RANDOM BROADBAND FILTERING

 

Xiaolin Wu; McMaster University

Dahua Gao; Xidian University

Qin Chen; Suzhou Institute of Nano-Tech and Nano-Bionics

Kaiwei Zhang; Xidian University

MA.PC.6

ESTIMATING THE SPATIAL RESOLUTION OF OVERHEAD IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS*

 

Haolin Liang; University of California, Merced

Shawn Newsam; University of California, Merced

TA.PG.2

UNDERWATER IMAGE SYNTHESIS FROM RGB-D IMAGES AND ITS APPLICATION TO DEEP UNDERWATER IMAGE RESTORATION*

 

Takumi Ueda; Tokyo University of Agriculture and Technology

Koki Yamada; Tokyo University of Agriculture and Technology

Yuichi Tanaka; Tokyo University of Agriculture and Technology

MQ.L5.2

LOW-COMPLEXITY TRANSFORM ADJUSTMENTS FOR VIDEO CODING

 

Amir Said; Qualcomm Technologies, Inc.

Hilmi Egilmez; Qualcomm Technologies, Inc.

Yung-Hsuan Chao; Qualcomm Technologies, Inc.

WA.L6.3

DEEP OBJECTIVE ASSESSMENT MODEL BASED ON SPATIO-TEMPORAL PERCEPTION OF 360-DEGREE VIDEO FOR VR SICKNESS PREDICTION*

 

Kihyun Kim; Korea Advanced Institute of Science and Technology (KAIST)

Sangmin Lee; Korea Advanced Institute of Science and Technology (KAIST)

Hak Gu Kim; Korea Advanced Institute of Science and Technology (KAIST)

Minho Park; Korea Advanced Institute of Science and Technology (KAIST)

Yong Man Ro; Korea Advanced Institute of Science and Technology (KAIST)

WP.L5.4

LIGHT FIELD COMPRESSION USING FOURIER DISPARITY LAYERS*

 

Elian Dib; INRIA

Mikael Le Pendu; Trinity College Dublin

Christine Guillemot; INRIA

TP.L5.5

DESIGNING RECURRENT NEURAL NETWORKS BY UNFOLDING AN L1-L1 MINIMIZATION ALGORITHM*

 

Hung Duy Le; Vrije Universiteit Brussel-imec

Huynh Van Luong; Vrije Universiteit Brussel-imec

Nikos Deligiannis; Vrije Universiteit Brussel-imec

TP.L5.6

EXACT INCREMENTAL AND DECREMENTAL LEARNING FOR LS-SVM

 

Wei-Han Lee; IBM Research

Bong Jun Ko; IBM Research

Shiqiang Wang; IBM Research

Changchang Liu; IBM Research

Kin Leung; Imperial College London

MQ.L5.1

IMPROVED QUANTIZATION AND TRANSFORM COEFFICIENT CODING FOR THE EMERGING VERSATILE VIDEO CODING (VVC) STANDARD

 

Heiko Schwarz; Fraunhofer Heinrich Hertz Institute / FU Berlin

Tung Nguyen; Fraunhofer Heinrich Hertz Institute

Detlev Marpe; Fraunhofer Heinrich Hertz Institute

Thomas Wiegand; Fraunhofer Heinrich Hertz Institute / TU Berlin

Marta Karczewicz; Qualcomm Inc.

Muhammed Coban; Qualcomm Inc.

Jie Dong; Qualcomm Inc.

TP.PF.8

DISTORTED REPRESENTATION SPACE CHARACTERIZATION THROUGH BACKPROPAGATED GRADIENTS*

 

Gukyeong Kwon; Georgia Institute of Technology

Mohit Prabhushankar; Georgia Institute of Technology

Dogancan Temel; Georgia Institute of Technology

Ghassan AlRegib; Georgia Institute of Technology

TA.L5.2

MULTI-TASK LEARNING WITH COMPRESSIBLE FEATURES FOR COLLABORATIVE INTELLIGENCE*

 

Saeed Ranjbar Alvar; Simon Fraser University

Ivan V. Bajic; Simon Fraser University

Best Paper Award for Industry

The Best Paper Award for Industry will be given to author(s) of an ICIP 2019 paper exhibiting exceptional industrial merit and potential industrial impact within the technical scope of the conference. The candidate(s) are not required to be IEEE members or to be working in industry. The prize shall consist of a monetary amount and a certificate. The award will be presented during IEEE ICIP 2019, and also announced on the conference website. The award will not be conferred in case no suitable candidates can be identified.

Best Industry Paper Finalists

Session

Title

TA.L5.1

LEARNING TO RENDER BETTER IMAGE PREVIEWS

 

Shumeet Baluja; Google Inc.

David Marwood; Google Inc.

Nick Johnston; Google Inc.

Michele Covell; Google Inc.

MQ.L5.2

LOW-COMPLEXITY TRANSFORM ADJUSTMENTS FOR VIDEO CODING

 

Amir Said; Qualcomm Technologies, Inc.

Hilmi Egilmez; Qualcomm Technologies, Inc.

Yung-Hsuan Chao; Qualcomm Technologies, Inc.

WQ.L6.1

5D VIDEO STABILIZATION THROUGH SENSOR VISION FUSION

 

Binnan Zhuang; xsense.ai

Dongwoon Bai; Samsung Semiconductor Inc

Jungwon Lee; Samsung Semiconductor Inc

WA.L5.2

INTERWEAVED PREDICTION FOR AFFINE MOTION COMPENSATION

 

Kai Zhang; Bytedance Inc.

Li Zhang; Bytedance Inc.

Hongbin Liu; Bytedance Inc.

Jizheng Xu; Bytedance Inc.

Yue Wang; Bytedance Inc.