CEFRL: Compact and Efficient Feature Representation and Learning

in Computer Vision 2017


in conjunction with ICCV2017

Venice, Italy, October 22~29 2017

Workshop Chairs

 

Name

Email

Affiliations

Li Liu

li.liu@oulu.fi

National University of Defense Technology, China

University of Oulu, Finland

Jie Chen

jiechen@ee.oulu.fi

University of Oulu, Finland

Zhen Lei

zlei@nlpr.ia.ac.cn

Chinese Academy of Sciences, China

Jiwen Lu

lujiwen@tsinghua.edu.cn

Tsinghua University, China

 Guoying Zhao

gyzhao@ee.oulu.fi

University of Oulu, Finland

Northwest University, China

Matti Pietikäinen

mkp@ee.oulu.fi

University of Oulu, Finland

 

Program

0800 Welcome

0810-0100 Session 1: Oral Session

·        [0810] Class-specific Reconstruction Transfer Learning via Sparse Low-rank Constraint, Shanshan Wang, Lei Zhang, Wangmeng Zuo

·        [0830] DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows, Jason Kuen, Xiangfei Kong, Gang Wang, Ya-Peng Tan

·        [0850] Invited Talk: Learning Invariant Descriptors, Pascal Fua (EPFL, Switzerland)

1000-1030 Morning Break (????) & Poster Session

·        Vehicle Logo Retrieval Based on Hough Transform and Deep Learning, Huan Li, Yujian Qin, Li Wang

·        P-TELU : Parametric Tan Hyperbolic Linear Unit Activation for Deep Neural Networks, Rahul Duggal, Anubha Gupta

·        Learning Efficient Deep Feature Representations via Transgenerational Genetic Transmission of Environmental Information during Evolutionary Synthesis of Deep Neural Networks,

Mohammad Javad Shafiee, Elnaz Barshan, Francis Li, Brendan Chwyl, Michelle Karg, Christian Scharfenberger, Alexander Wong

·        Large-Scale Content-Only Video Recommendation, Joonseok Lee, Sami Abu-El-Haija

·        Efficient Fine-grained Classification and Part Localization Using One Compact Network, Xiyang Dai, Ben Southall, Nhon Trinh, Bogdan Matei

·        Structured Images for RGB-D Action Recognition, Pichao Wang, Shuang Wang, Zhimin Gao, Yonghong Hou, Wanqing Li

·        Compact Feature Representation for Image Classification Using ELMs, Dongshun Cui, Guanghao Zhang, Wei Han, Liyanaarachchi Lekamalage Chamara Kasun, Kai Hu, Guang-Bin Huang

·        Improved Descriptors for Patch Matching and Reconstruction, Rahul Mitra, Jiakai Zhang, Sanath Narayan, Shuaib Ahmed, Sharat Chandran, Arjun Jain

·        Compact color texture descriptor based on rank transform and product ordering in the RGB color space, Antonio Fernández, David Lima, Francesco Bianconi, Fabrizio Smeraldi

·        Spatial-Temporal Weighted Pyramid using Spatial Orthogonal Pooling, Yusuke Mukuta, Yoshitaka Ushiku, Tatsuya Harada

·        Double-task Deep Q-Network with Multiple views, Jun Chen, Tingzhu Bai, Xiangsheng Huang, Xian Guo, Jianing Yang, Yuxing Yao

·        Automatic discovery of discriminative parts as a quadratic assignment problem, Ronan Sicre, Julien Rabin, Yannis Avrithis, Teddy Furon, Frederic Jurie, Ewa Kijak

·        UDNet: Up-Down Network for Compact and Efficient Feature Representation in Image Super-Resolution, Chang Chen, Xinmei Tian, Zhiwei Xiong, Feng Wu

·        Enlightening Deep Neural Networks with Knowledge of Confounding Factors, Yu Zhong, Gil Ettinger

·        Consistent Iterative Multi-view Transfer Learning for Person Re-identification, Cairong Zhao, Xuekuan Wang, Yipeng Chen, Can Gao, Wangmeng Zuo, Duoqian Miao

·        Binary-decomposed DCNN for accelerating computation and compressing model without retraining, Ryuji Kamiya, Takayoshi Yamashita, Mitsuru Ambai, Ikuro Sato, Yuji Yamauchi, Hironobu Fujiyoshi

·        Co-localization with Category-Consistent Features and Geodesic Distance Propagation, Hieu M Le, Chen-Ping Yu, Gregory Zelinsky , Dimitris Samaras

·        End-to-End Visual Target Tracking in Multi-Robot Systems Based on Deep Convolutional Neural Network, Yawen Cui, Bo Zhang , Wenjing Yang, Zhiyuan Wang, Yin Li, Xiaodong Yi, Yuhua Tang

·        Oceanic Scene Recognition Using Graph-of-Words (GoW), Xinghui Dong, Junyu Dong

·        Coarse-to-Fine Deep Kernel Networks, Hichem Sahbi

·        Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet, Amarjot Singh, Nick Kingsbury

·        4D Effect Video Classification with Shot-aware Frame Selection and Deep Neural Networks, Thomhert S Siadari, Mikyong Han, Hyunjin Yoon

·        Max-Boost-GAN: Max Operation to Boost Generative Ability of Generative Adversarial Networks, XINHAN DI, Pengqian Yu

·        Multiplicative Noise Channel in Generative Adversarial Networks, XINHAN DI, Pengqian Yu

·        Fast CNN-based document layout analysis, Dario A B Oliveira, Matheus Viana

·        Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation

Amarjot Singh; Devamanyu Hazarika; Aniruddha Bhattacharya

1030-1210 Session 2: Oral Session II 

·        [1050] Video Summarization via Multi-View Representative Selection, Jingjing Meng, Suchen Wang, Hongxing Wang, Junsong Yuan, Ya-Peng Tan 

·        [1110] Dynamic Computational Time for Visual Attention, Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, Wei Xu

·        [1130] Rotation Invariant Local Binary Convolution Neural Networks, Xin Zhang, Liu Li, Yuxiang Xie, Jie Chen, Lingda Wu, Matti Pietikäinen

·        [1150] The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations through Sexual Evolutionary Synthesis,

Audrey Chung, Mohammad Javad Shafiee, Paul Fieguth, Alexander Wong

 

·        [1400] Invited Talk: Local feature detectors and descriptors in the era of deep learning: practical and theoretical progress, Andrea Vedaldi (University of Oxford)

1510 Afternoon Break (????)& Poster Session

·        Vehicle Logo Retrieval Based on Hough Transform and Deep Learning, Huan Li, Yujian Qin, Li Wang

·        P-TELU : Parametric Tan Hyperbolic Linear Unit Activation for Deep Neural Networks, Rahul Duggal, Anubha Gupta

·        Learning Efficient Deep Feature Representations via Transgenerational Genetic Transmission of Environmental Information during Evolutionary Synthesis of Deep Neural Networks,

Mohammad Javad Shafiee, Elnaz Barshan, Francis Li, Brendan Chwyl, Michelle Karg, Christian Scharfenberger, Alexander Wong

·        Large-Scale Content-Only Video Recommendation, Joonseok Lee, Sami Abu-El-Haija

·        Efficient Fine-grained Classification and Part Localization Using One Compact Network, Xiyang Dai, Ben Southall, Nhon Trinh, Bogdan Matei

·        Structured Images for RGB-D Action Recognition, Pichao Wang, Shuang Wang, Zhimin Gao, Yonghong Hou, Wanqing Li

·        Compact Feature Representation for Image Classification Using ELMs, Dongshun Cui, Guanghao Zhang, Wei Han, Liyanaarachchi Lekamalage Chamara Kasun, Kai Hu, Guang-Bin Huang

·        Improved Descriptors for Patch Matching and Reconstruction, Rahul Mitra, Jiakai Zhang, Sanath Narayan, Shuaib Ahmed, Sharat Chandran, Arjun Jain

·        Compact color texture descriptor based on rank transform and product ordering in the RGB color space, Antonio Fernández, David Lima, Francesco Bianconi, Fabrizio Smeraldi

·        Spatial-Temporal Weighted Pyramid using Spatial Orthogonal Pooling, Yusuke Mukuta, Yoshitaka Ushiku, Tatsuya Harada

·        Double-task Deep Q-Network with Multiple views, Jun Chen, Tingzhu Bai, Xiangsheng Huang, Xian Guo, Jianing Yang, Yuxing Yao

·        Automatic discovery of discriminative parts as a quadratic assignment problem, Ronan Sicre, Julien Rabin, Yannis Avrithis, Teddy Furon, Frederic Jurie, Ewa Kijak

·        UDNet: Up-Down Network for Compact and Efficient Feature Representation in Image Super-Resolution, Chang Chen, Xinmei Tian, Zhiwei Xiong, Feng Wu

·        Enlightening Deep Neural Networks with Knowledge of Confounding Factors, Yu Zhong, Gil Ettinger

·        Consistent Iterative Multi-view Transfer Learning for Person Re-identification, Cairong Zhao, Xuekuan Wang, Yipeng Chen, Can Gao, Wangmeng Zuo, Duoqian Miao

·        Binary-decomposed DCNN for accelerating computation and compressing model without retraining, Ryuji Kamiya, Takayoshi Yamashita, Mitsuru Ambai, Ikuro Sato, Yuji Yamauchi, Hironobu Fujiyoshi

·        Co-localization with Category-Consistent Features and Geodesic Distance Propagation, Hieu M Le, Chen-Ping Yu, Gregory Zelinsky , Dimitris Samaras

·        End-to-End Visual Target Tracking in Multi-Robot Systems Based on Deep Convolutional Neural Network, Yawen Cui, Bo Zhang , Wenjing Yang, Zhiyuan Wang, Yin Li, Xiaodong Yi, Yuhua Tang

·        Oceanic Scene Recognition Using Graph-of-Words (GoW), Xinghui Dong, Junyu Dong

·        Coarse-to-Fine Deep Kernel Networks, Hichem Sahbi

·        Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet, Amarjot Singh, Nick Kingsbury

·        4D Effect Video Classification with Shot-aware Frame Selection and Deep Neural Networks, Thomhert S Siadari, Mikyong Han, Hyunjin Yoon

·        Max-Boost-GAN: Max Operation to Boost Generative Ability of Generative Adversarial Networks, XINHAN DI, Pengqian Yu

·        Multiplicative Noise Channel in Generative Adversarial Networks, XINHAN DI, Pengqian Yu

·        Fast CNN-based document layout analysis, Dario A B Oliveira, Matheus Viana

·        Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation

Amarjot Singh; Devamanyu Hazarika; Aniruddha Bhattacharya

1600-1710 Session 3: Oral Session III

·        [1600] Few-Shot Hash Learning for Image Retrieval, Yu-Xiong Wang, Liangke Gui, Martial Hebert

·        [1620] A Handcrafted Normalized-Convolution Network for Texture Classification, Vu-Lam Nguyen, Ngoc-Son Vu, Philippe-Henri Gosselin

·        [1640] Towards Good Practices for Image Retrieval Based on CNN Features, omar seddati, stéphane dupont, Said Mahmoudi, Mahnaz pariyaan       

1700 Concluding Remarks

 

 

Submission

·       Paper submission is now open (Double blind review).

·       The authors will submit full length papers (ICCV format) on-line, including (1) Title of paper & short abstract summarizing the main contribution, (2) Contributions must be written and presented in English, and (3) The paper in PDF format. All submissions will be peer-reviewed by at least 3 members of the program committee.

Topics

We encourage researchers to study and develop new compact and efficient feature representations that are fast to compute, memory efficient, and yet exhibiting good discriminability and robustness. We also encourage new theories and applications related to features for dealing with these challenges. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

·       New features (handcrafted features, simpler and novel DCNN architectures, and feature learning in supervised, weakly supervised or unsupervised way) that are fast to compute, memory efficient and suitable for large-scale problems;

·       New compact and efficient features that are suitable for wearable devices (e.g., smart glasses, smart phones, smart watches) with strict requirements for computational efficiency and low power consumption;

·       Evaluations of current traditional descriptors and features learned by deep learning;

·       New applications of existing features in different domains, e.g. medical domain;

·       Other applications in different  domains, such as one dimension (1D) digital signal processing, 2D images, 3D videos and 4D videos;

 

Motivation

The goal of the CEFRL Workshop 2017 is to accelerate the study of compact and efficient feature representation and learning approaches in computer vision problems. We have entered the era of big data. The explosion of available visual data raises new challenges and opportunities. One major challenge is how to intelligently analyze and understand the unprecedented scale of visual data. Furthermore, mobile/wearable devices such as mobile phones and smart glasses are ubiquitous throughout our surroundings. Applications of feature representation technologies have to handle large-scale data or to run on mobile/wearable devices with limited computational capabilities and storage space, hence there is a growing need for feature descriptors that are fast to compute, memory efficient, and yet exhibiting good discriminability and robustness. This problem becomes more difficult when the data show various types of variations such as noise, illumination, scale, rotation and occlusion.

 

Important Dates

Event

Date

Paper Submission Deadline

July 31, 2017

Notification of Acceptance

August 22, 2017

Camera-ready due

August 24, 2017

Workshop (Full Day)

October 28, 2017

 

 

 

Program outline (full day)

Time

Event

9:00~9:45

Invited Talk (Talk 1)

9:50~10:35

Invited Talk (Talk 2)

10:35~11:15

Oral Session (2 presentations: 20min each)

11:15~11:30

Coffee break

11:30~12:10

Oral Session (2 presentations: 20min each)

12:10~14:00

Lunch

14:00~15:00

Invited Talk (Talk 3)

15:00~16:00

Poster Session (12 posters)

16:00~17:00

Oral Session (3 presentations: 20min each)

17:00~17:15

Closing Remarks

Invited Speakers:

     Efficient Features for Visual Recognition

Professor Pascal Fua

EPFL, Switzerland

Email: pascal.fua@epfl.ch

 

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and the Ph.D. degree in Computer Science from the University of Orsay in 1989. He then worked at SRI International and INRIA Sophia-Antipolis as a Computer Scientist. He joined EPFL in 1996 where he is now a Professor in the School of Computer and Communication Science and heads the Computer Vision Laboratory. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. He has (co)authored over 200 publications in refereed journals and conferences. He is an IEEE Fellow and has been an associate editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. His papers have currently over 27,400 citations in Google Scholar (h-index 76). He often serves as program committee member, area chair, and program chair of major vision conferences.

 

 

 

 

 

 

 

Learning and Understanding Deep Visual Representations

Professor Andrea Vedaldi

University of Oxford, UK

Email: vedaldi@robots.ox.ac.uk

Andrea Vedaldi received the BSc degree from the Information Engineering Department, University of Padua, Italy, in 2003 and the MSc and PhD degrees from the Computer Science Department, University of California at Los Angeles, in 2005 and 2008. Since 2008 he joined the University of Oxford where he is now an Associate Professor in Engineering Science. His research interests include detection and recognition of visual object categories, visual representations, and large scale machine learning applied to computer vision. He is the recipient of the “Outstanding Doctor of Philosophy in Computer Science” and “Outstanding Master of Science in Computer Science” awards of University of California at Los Angeles. In 2014, he was awarded an ERC Starting grant on “Integrated and Detailed Image Understanding”. He won the best paper award at BMVC2014. His work for VLFeat was awarded the PAMI Mark Everingham Prize at  ICCV2015.

 

 

 

 

 

 

 

Towards biologically plausible deep learning

Professor Yoshua Bengio 

Université de Montréal, Canada

Email: yoshua.bengio@umontreal.ca

Yoshua Bengio received a PhD in Computer Science from McGill University, Canada in 1991. After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun and Vladimir Vapnik, he became professor at the Department of Computer Science and Operations Research at Université de Montréal. He is the author of two books and more than 200 publications, the most cited being in the areas of deep learning, recurrent neural networks, probabilistic learning algorithms, natural language processing and manifold learning. He is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since year 2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since year 2006 an NSERC Industrial Chair, since year 2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the new International Conference on Learning Representations. His current interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning and representation learning, the geometry of generalization in high-dimensional spaces, manifold learning, biologically inspired learning algorithms, and challenging applications of statistical machine learning. His papers have currently over 58,000 citations in Google Scholar (h-index 91).

 

 

Program Committee:

No.

Name

Affiliation

1

Navneet Dalal

Flutter, USA

2

Svetlana Lazebnik

Illinois, USA

3

Jingdong Wang

Microsoft Asia, China

4

Krystian Mikolajczyk

Imperial College London, UK

5

Xiaogang Wang

Chinese University of HongKong, China

6

Xiaoyang Tan

Nanjing, China

7

Bill Triggs

INRIA, France

8

Lei Zhang

Hong Kong Polytechnic University,  China

9

Wanli Ouyang

Chinese University of HongKong, China

10

Bolei Zhou

MIT, USA

11

Liang Wang

Chinese Academy of Sciences, China

12

Jianxin Wu

Nanjing University, China

13

Jakob Verbeek

INRIA, France

14

Tinne Tyutelaars

KU Leuven, the Netherlands

15

Kristin Dana

Rutgers University, USA

16

Xilin Chen

Chinese Academy of Sciences, China

17

Ross Girshick

Facebook AI Research, USA

18

Jingdong Wang

Microsoft Research Asia, China

19

Paul Fieguth

University of Waterloo, Canada

20

Dacheng Tao

University of Technology Sydney, Australia

21

Lewis Griffin

University College London, UK

22

Chunhua Shen

University of Adelaide, Australia

23

Cordelia Schmid

INRIA, France

24

Luc Van Gool

ETHZ, Switzerland

25

Xiaopeng Hong

University of Oulu, Finland

26

Miguel Bordallo

University of Oulu, Finland

27

Hazım Kemal Ekenel

Istanbul Technical University, Turkey

28

ChuSong Chen

Academia Sinica, Taiwan

 

 

Contact

Li Liu

Email: li.liu@oulu.fi , dreamliu2010@gmail.com

National University of Defense Technology, China

Center for Machine Vision Research (CMVS),

University of Oulu, Finland

Program

To be done.

 

 

 

 

 


 

 


 

 

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