RoF: Robust Features for Computer Vision


in conjunction with CVPR2016

Las Vegas, Nevada, June 26, 2016

Program (NEW)

See the end of this page.

Submission

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

2.      The authors will submit full length papers (CVPR 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 develop new robust features (e.g., manually designed local features or feature learning) to extract useful feature representations. We also encourage new theories and processes related to features. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

  1. New features (manually designed local features, or feature learning in supervised, weakly supervised or unsupervised way) robust to noise, illuminations, scale, rotations and occlusions,
  2. Robust features that are suitable for wearable devices (e.g., smart glasses, smart phones) with strict requirements for computational efficiency and low power consumption,
  3. New applications of descriptors in different domains, e.g. medical domain,
  4. Other application in different  domain, such as one dimension (1D) digital signal processing, 2D images, 3D videos and 4D videos,
  5. Evaluations of current traditional descriptors,
  6. Evaluations between the features learned by deep learning and the traditional descriptors (e.g., LBP, SIFT, HOG)

 

Motivation

The goal of the RoF Workshop 2016 is to accelerate the study of robustness of local descriptors in computer vision problems. With the increase of acceleration of digital photography and the advances in storage devices over the last decade, we have seen explosive growth in the available amount of visual data and equally explosive growth in the computational capacities for data understanding. How to extract robust representations for many computer vision tasks is still a challenging problem. This problem becomes more difficult when the data show different types of variations, e.g., noise, illuminations, scale, rotations and occlusions.

 

Important Dates

  1. Paper Submission: March 8, 2016, March 22, 2016
  2. Notification of acceptance: April 20, 2016
  3. Camera-ready paper: May 2nd, 2016
  4. Workshop (half day): July 1, 2016

 

Workshop Chairs

 

Invited Speakers:

1)      Deep features for face recognition and related problems

Prof. Rama Chellappa, University of Maryland, USA

This talk will cover their recent work on using deep features for face verification, recognition, age estimation, face detection, alignment and expression recognition.

 

2)      Convolutional patch representations for image retrieval

Dr. Cordelia Schmid, INRIA, FRANCE

 

Program Committee:

·           Aleix Martinez, Ohio State University, USA

·           Alice Caplier, Grenoble, France

·           Bin Fan, Chinese Academy of Sciences, China

·           Baochang Zhang Beihang University, China

·           Engin Tola, Aurvis R&D, Turkey

·           Enrique Alegre, University of León, Spain

·           Francesca Odone, university of Genova, Italy

·           Giovanni Fusco, Smith-Kettlewell Eye Research Institute, USA

·           Huu Tuan NGUYEN, Grenoble, France

·           Hazim Kemal Ekenel, Karlsruhe Institute of Technology, Germany

·           Ioannis Patras Queen Mary University, UK

·           Jean-Luc, Dugelay, Eurecom, France

·           Juho Kannala, University of Oulu, Finland

·           Jun Yang, Northwestern Polytechnical University, China

·           Lijun Yin, Binghamton University, USA

·           Lei Zhang, Hong Kong Polytechnic University,  Hong Kong, China

·           Loris Nanni, University of Padua (Padova), Italy

·           Michael Teutsch, Fraunhofer IOSB, Germany

·           Motilal Agrawal, Menlo Park, CA, USA

·           Nicoletta Noceti, University of Genova, Italy

·           Rainer Lienhart, Universität Augsburg, Germany

·           Ruiping Wang, Chinese Academy of Sciences, China

·           Rocio A Lizarraga-Morales, Universidad de Guanajuato DICIS, Mexico

·           Sei-ichiro Kamata, Waseda University, Japan

·           Shu Liao, Siemens, USA

·           Shengcai Liao, NLPR, Chinese Academy of Sciences, China

·           Tiago de Freitas Pereira, University of Campinas (UNICAMP), Brazil

·           Tri Huynh, Eurecom, France

·           Wenchao Zhang, Nanyang technological university, Singapore

·           Xianbiao Qi, Beijing University of Posts and Telecommunications, China

·           Xiaoyang Tan, Nanjing University of Aeronautics and Astronautics, China

·           Xiujuan Chai, ICT, Chinese Academy of Sciences, China

·           Xiaopeng Hong, University of Oulu, Finland

 

 

Contact

Jie Chen

Email: rolod2014@gmail.com

Center for Machine Vision Research (CMV),

University of Oulu, Finland

Program

(The following program was submitted to CVPR. please refer to CVPR program as the final schedule)

 


Session
(Optional)

Start
Time

Paper/Talk Title

Author/Speaker

 

800

Welcome

 

Oral session 1

 

 

 

 

810

Invited Talk1: Convolutional patch representations for image retrieval

 Dr. Cordelia Schmid (INRIA, France)

 

850

Fast Image Gradients Using Binary Feature Convolutions

Pierre-Luc St-Charles, Guillaume-Alexandre Bilodeau, Robert Bergevin

 

910

Texture Complexity based Redundant Regions Ranking for Object Proposal

Wei Ke, Tianliang Zhang, Jie Chen, Fang Wan, Qixiang Ye

 

930

Deeply Exploit Depth Information for Object Detection

Saihui Hou

 

 

 

 

Poster session

950

Poster session - including coffee

 

 

 

Efficient Deep Feature Learning and Extraction via StochasticNets

Mohammad Javad Shafiee, Parthipan Siva, Paul Fieguth, Alexander Wong

 

 

Embedding Sequential Information into Spatiotemporal Features for Action Recognition

Yuancheng Ye, YingLi Tian

 

 

Learning Discriminative Features with Class Encoder

Hailin Shi

 

 

Do We Need Binary Features for 3D Reconstruction?

Bin Fan, Qingqun Kong, Wei Sui, Zhiheng Wang, Xinchao Wang, SHIMING XIANG, Chunhong Pan, Pascal Fua

 

 

Deep Features or Not: Temperature and Time Prediction in Outdoor Scenes

Anna Volokitin, Radu Timofte, Luc Van Gool

 

 

Euclidean and Hamming Embedding for Image Patch Description with Convolutional Networks

Zishun Liu, Zhenxi Li, Juyong Zhang, Ligang Liu

 

 

Robust 2DPCA and Its Application

Quanxue Gao, Qianqian Wang

 

 

Background Subtraction using Local SVD Binary Pattern feature

Li Li Guo, Xin Liu, Zhen Ping Qiang, Dan Xu

 

 

Generating Discriminative Object Proposals via Submodular Ranking

Zhang Yangmuzi, Zhuolin Jiang, Xi Chen, Larry Davis

 

 

Time series representation and similarity based on local autopatterns

Mustafa Gökçe Baydoğan, George Runger

 

 

 

 

Oral session 2

 

 

 

 

1050

Keynote Talk 2: Deep features for face recognition and related problems

Prof. Rama Chellappa (University of Maryland, USA)

 

1130

Improving Gradient Histogram Based Descriptors for Pedestrian Detection in Datasets with Large Variations

Prashanth Balasubramanian, Sarthak Pathak, Anurag Mittal

 

1150

Unsupervised Robust Feature-based Partition Ensembling to Discover Categories

Roberto Javier López-Sastre

 

1210

The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition

Zhenzhong Lan, Shoou-I Yu, Dezhong Yao, Ming Lin, Bhiksha Raj, Alexander Hauptmann

 

1230

concluding remarks

 

 

1235

Lunch (on your own)

 

 

 


 

 

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