4th International Workshop on

Compact and Efficient Feature Representation and Learning in Computer Vision 2019

in conjunction with ICCV 2019
Seoul, Korea, October 27~November 2 2019

Feature representation is at the core of many computer vision and pattern recognition applications such as image classification, object detection, image and video retrieval, image matching and many others. For years, milestone engineered feature descriptors such as SIFT, SURF, HOG and LBP have dominated various domains of computer vision. The design of feature descriptors with low computational complexity has gained lots of attention and a number of efficient descriptors including BRIEF, FREAK, BRISK and DAISY have been presented. In the past few years we have witnessed significant progress in feature representation and learning. The popularity of traditional handcrafted features seems to be overtaken by the Deep Convolutional Neural Networks (DeepCNNs), which can learn powerful features automatically from data and have brought about breakthroughs in various problems in computer vision. However, these advances rely on deep networks with millions or even billions of parameters, and the availability of GPUs with very high computation capability and large scale labeled datasets plays a key role in their success. In other words, powerful DeepCNNs are data hungry and energy hungry.

Nowadays, given the exponentially increasing number of images and videos, the emerging phenomenon of big dimensionality exposes the inadequacies of existing approaches, no matter whether traditional handcrafted features or recent deep learning-based ones. Thus, there is a pressing need for new scalable and efficient approaches that can cope with this explosion of dimensionality.

In addition, with the prevalence of social media networks and the portable / mobile / wearable devices to access them, comes the current concern of the limited resources (e.g., battery life, memory, storage space, computational power, and bandwidth) these offer. The demands on sophisticated portable / mobile / wearable device applications in handling large-scale visual data is rising. In such applications, real time performance is of the utmost importance to users, since no one is willing to spend any time waiting nowadays. Therefore, there is a growing need for feature descriptors that are fast to compute, memory efficient, and that yet exhibit good discriminability and robustness.

Given sufficient annotated data, existing features - especially those produced by deep CNNs - have yielded good performance. Nonetheless, there are many applications where only limited amounts of annotated training data can be gathered (such as with many visual inspection or medical diagnostics tasks). Such applications are challenging for many existing feature representations, and require sample-efficient techniques to learn good representations.

A number of efforts, such as compact binary features, DCNN network quantization and compression, energy efficient network architectures, binary hashing techniques and data efficient techniques like meta learning, have appeared at top conferences (including CVPR, ICCV, ECCV, NIPS and ICLR) and top journals (including TPAMI and IJCV). The workshop aims at stimulating computer vision researchers to discuss the next steps in this important research area.

Important Dates(Tentative)

Event Date
Paper Submission DeadlineJuly 30 August 7, 2019
Notification of AcceptanceAugust 25, 2019
Camera-ready dueAugust 30, 2019
Workshop (Full day)October 27 November 2, 2019


We encourage researchers to study and develop new feature representations that are fast to compute, memory efficient, and data efficient, while exhibiting good discriminability and robustness. We also encourage the presentation of new theories and applications related to feature representation and learning 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:

1. New features (handcrafted features, lightweight network architectures, deep model compression/quantization, and feature learning in supervised, weakly supervised or unsupervised way) that are fast to compute, memory efficient and suitable for large scale problems;

2. 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;

3. Hashing/binary codes learning and its related applications in different domains, e.g., content-based retrieval;

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

5. Hybrid methods combining strengths of handcrafted and learning based approaches;

6. Sample-efficient feature learning methods, e.g., meta learning, few shot learning;

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

Invited Speakers

(1) Professor Ikeuchi Katsushi (Confirmed)
University of Tokyo, Japan, (Email: katsushi.ikeuchi@outlook.jp)

Katsushi Ikeuchi received the BE degree from Kyoto University in 1973 and the PhD degree from the University of Tokyo in 1978. After working at the MIT AI Laboratory for three years, ETL for five years, and CMU Robotics Institute for 10 years, University of Tokyo Institute of Industrial Science for 19 years, he joined Microsoft Research Asia in 2015 as a principal researcher. His research interests include computer vision, robotics, and computer graphics. In these research fields, he has received several awards, including the David Marr Prize in computational vision for the paper “Shape from Interreflection”, and “IEEE Robotics and Automation Society”. K. S. Fu memorial best transaction paper award. In addition, in 1992, his paper “Numerical Shape from Shading and Occluding Boundaries” was selected as one of the most influential papers to have appeared in the Artificial Intelligence Journal within the past 10 years. His community service includes general chair of IROS 95, ITSC 99, IV 01, ICCV 05, ACCV 07; program chair of CVPR 96, ICCV 03, ICRA 09, ICPR 12, ICCV 15; associate editor of IEEE TRA, IEEE TPAMI; and a distinguished lecturer of the Signal Processing Society in 2000-2002 and Robotics and Automation Society in 2004-2006. He is the editor-in-chief of IJCV. Through these research and society service, he was awarded a fellow from the IEEE, IEICE, IPSJ and RSJ. He received the Distinguished Researcher Award from IEEE PAMI, Medal of Honor with Purple Ribbon from Japanese Emperor, and the Okawa award from Okawa foundation.

(2) Professor Ramin Zabih (Confirmed)
the Cornell Tech, the United States (rdz@cs.cornell.edu)

Ramin Zabih received undergraduate degrees from MIT in computer science and math, and the PhD degree from Stanford in computer science. He is a professor of computer science at Cornell University at Cornell NYC Tech. He and the students developed graph cut methods for computer vision that have been widely used in both academia and industry. He received the Helmholtz Prize at ICCV in 2013, the Koenderink prize at ECCV in 2012, and Best Paper Awards at ECCV in 2002. He was a program chair for CVPR in 2007 and a general chair for CVPR in 2013, and will be a general chair for ECCV in 2018. He served as an editor-in-chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009-2012, and since 2013 has chaired the PAMI TC. He is also the president of the nonprofit Computer Vision Foundation. He is a fellow of the ACM and the IEEE.

Program outline

Time Event
8:55~9:00Welcome Introduction
9:00~9:45Invited Talk (Talk 1)
9:50~10:35Invited Talk (Talk 2)
10:35~11:15Oral Session (2 presentaions: 20min each)
11:15~11:30Coffee break
11:30~12:10Oral Session (2 presentations: 20min each)
14:15~15:00Invited Talk (Talk 3)
15:00~16:00Poster Session
16:00~17:00Oral Session (3 presentations: 20min each)
17:00~17:15Closing Remarks

Oral Session 1 (10:35~11:15)

Oral Session 2 (11:30~12:10)

Oral Session 3 (16:00~17:00)

Poster Session (15:00~16:00)

Paper Submission Information

All submissions will be handled electronically via the workshop’s CMT Website. Click the following link to go to the submission site: https://cmt3.research.microsoft.com/CEFRL42019.

Papers should describe original and unpublished work about the related topics. Each paper will receive double blind reviews, moderated by the workshop chairs. Authors should take into account the following:

The authors will submit full length papers (ICCV format) online, including:

(1) Title of paper and short abstract summarizing the main contribution,

(2) Names and contact info of all authors, also specifying the contact author,

(3) Contributions must be written and presented in English and

(4) The paper in PDF format.

All submissions will be peer-reviewed by at least 3 members of the program committee.

Poster guideline: The dimension of poster panels is 1950mm (width) x 950mm (height). It is the same as that of the main conference.


Dr. Li Liu
(University of Oulu & NUDT)
Dr. Yu Liu
(PSI group of KU Leuven)
Dr. Wanli Ouyang
(Univeristy of Sydney)
Dr. Jiwen Lu
(Tsinghua University)
Prof. Matti Pietikäinen
(University of Oulu)

Previous CEFRL Workshop

· 3rd CEFRL Workshop in conjunction with CVPR 2019

· 2nd CEFRL Workshop in conjunction with ECCV 2018

· 1st CEFRL Workshop in conjunction with ICCV 2017

Please contact Li Liu if you have question. The webpage template is by the courtesy of awesome Georgia.