Dr. Michael S. Lew

Head, Deep Learning and Computer Vision Research Group
Director, LIACS Media Lab
co-Head, Computer Systems, Imagery and Media Research Cluster
Computer Science Department (LIACS)
Leiden University




My Positions and Research

  • Co-head of the LIACS Computer Systems, Imagery and Media Research cluster (30+ members), which is one of the two main research groups at the computer science department at Leiden University.

  • Chair Full Professor at Tsinghua University (US News Ranking for Computer Science | Ranking Eng. | Archive | in China | ARWU | ARWU-CS), 2003- (month-long visits)

  • Tenured associate professor at Leiden University, 2001-

    Research - short version

    My research lies in the field of artificial intelligence and the sub-fields of deep learning and computer vision. I develop new deep learning algorithms for automatic image annotation and exploiting synergy between the user and the computer in novel interactive and intelligent search paradigms.

    Research - longer version

    My research lies in the field of artificial intelligence and the sub-fields of deep learning and computer vision. My goal is to endow computers with all of the capabilities of the human visual system. The most promising approach is the paradigm of deep learning which involves developing and training deep convolutional neural networks for each visual task using large training sets. The deep learning and computer vision algorithms I develop are typically used for solving problems in the area of multimedia retrieval which covers the fundamentals (theory and practice) of searching for visual/image information along with additional media such as text and audio (multimedia) where the primary hurdle is the semantic gap. Multimedia retrieval provides the urgency in both societal and scientific challenges. It also ensures that humans are always in the loop because humans create the queries for information.

    Because humans want to express their search queries using high level human concepts, the grand challenge in multimedia retrieval has been "bridging" the semantic gap which is the gap between the high level concepts of humans and the low level features from images. The problem of image classification is especially important because it can bridge the semantic gap by taking an input image and outputting human level concepts. If we can solve the semantic gap, then it would make all of the collected art/heritage, scientific, WWW and personal images searchable and accessible.

    Teaching/Courses

    LIACS News Research Grants (July 2017)
    • Computer Aided Medical Diagnosis Using Big Data - NVIDIA, CSC, 1 PhD

    • Data Mining on High Volume Simulation Output - NWO, 3 PhDs

    • Deep Visual Understanding of Paintings and Pictures - NVIDIA, CSC, 1 PhD

    • Making Sense of Illustrated Handwritten Archives - NWO, 2 PhDs, 1 postdoc

    • Automating archaeological object detection in remotely sensed data, Data Science Research Programme, 1 PhD (in archaeology department)

    • Transmedia storytelling voor kritisch engagement - NWO, 1 assistant/associate professor

    Influential People Activities (selected) University & Department Positions
    • co-Head of the Imagery and Media Research Cluster - One of the 4 research themes in the Computer Science (CS) dept.

    • Chair, LIACS MSc Education Committee

    • Member (and 2nd chair) of the Board of Examiners (ExamenCommissie) - oversees the quality of all CS dept. courses (until 2014)

    • Member of the Curriculum Committee - guides the direction and content of the CS dept. courses

    • Member of the Organizing Team for the LIACS Research Seminar

    • co-Director of the LIACS Media Lab - advises and guides research and teaching in multimedia technology within the CS dept.

    • Member of the LIACS Scientific Council - steering committee for all research in the CS dept.
    Deep Learning Publications - Recent (At this weblink are some tips for starting points and a nice taxonomy is at CNN Taxonomy)
    • SWAPGAN: A Multistage Generative Approach for Person-to-Person Fashion Style Transfer, IEEE Transactions on Multimedia, 2019

    • Bag of Surrogate Parts Feature for Visual Recognition, IEEE Transactions on Multimedia, volume 20: 1525-1536, 2018

    • CycleMatch: A Cycle-consistent Embedding Network for Image-Text Matching, Pattern Recognition, volume 93, 2019

    • Learning visual and textual representations for multimodal matching and classification, Pattern Recognition, volume 84: 51-67, 2018

    • A comprehensive evaluation of local detectors and descriptors, Signal Processing: Image Communication, Volume 59, November Pages 150-167, 2017

    • Deep Binary Codes for Large Scale Image Retrieval, Neurocomputing, 2017

    • Deep learning for visual understanding, Neurocomputing, 2016

    • Learning a Recurrent Residual Fusion Network for Multimodal Matching, ICCV - IEEE Int. Conf. Computer Vision, 2017

    • On the Exploration of Convolutional Fusion Networks for Visual Recognition, MMM -- International Conference on MultiMedia Modeling, 2017, best paper award

    • Improving the Discrimination between Foreground and Background for Semantic Segmentation, ICIP -- IEEE Int. Conf. Image Processing, 2017

    • Learning Relaxed Deep Supervision for Better Edge Detection, CVPR - IEEE Conf. Computer Vision and Pattern Recognition, 2016

    • Bag of Surrogate Parts: one inherent feature of deep CNNs, BMVC - British Machine Vision Conference, 2016

    • DeepIndex for Accurate and Efficient Image Retrieval, ACM ICMR -- Int. Conf. Multimedia Retrieval, 2015

    Editorships Books

    Conference Organization

    Scientific Conference Program Committees

    Representative Publications (over 180 peer-reviewed in ACM, IEEE, and LNCS)
         Browse LIACS publications

    Selected Past Research Milestones

        - My first IEEE Trans PAMI article (in the special issue on Learning in Computer Vision), 1994

        - My first Netherlands Research (SION/NWO) Grant Proposal (ranked #1 over all submissions that year), 1995

        - My first position as Chair Full Professor at Tsinghua (first contract), 2003

        - My first General co-Chair service for the International Conference on Image and Video Retrieval (CIVR), 2003

    Recent Graduate Students

    • Mingrui Lao
    • Nan Pu
    • Wei Chen
    • Wouter B. Verschoof-van der Vaart, DSRP Grant
    • Theodoris Georgiou, NWO Grant
    • Yu Liu (graduating with Ph.D. on October 24th, 2018)
    • Yanming Guo (graduated with Ph.D. on October 5th, 2017)
    • Song Wu (graduated with Ph.D. on December 22nd, 2016)
    • Susan Laraghy
    • Zhenyang Li
    • Ran Tao
    • Simon Zaaijer
    • Ard Oerlemans (graduated with Ph.D. on December 22nd, 2011)
    • Bart Thomee (graduated with Ph.D. on November 3rd, 2010)
    • Nicu Sebe (graduated with Ph.D. on March 28th, 2001)

    Interesting Projects

    Old Research Projects (funded)
    • CSC - Multimedia Information Retrieval
    • CSC - Visual Learning Using Big Data
    • BRICKS Project (member of the project board) - Interactive Search and Browsing
    • Cyttron (co-leader of the BioSearch Project) - Multi-modal imaging search
    • Advanced Information Processing in Bioinformatics, NBIC Biorange IV - VL-e (investigator)
    • VIRSI Project (project leader) - Computational Imagination for Intelligent Search
    • Philips Research Project on Bio-Medical Analysis (project leader)
    • Philips Research Project on Texture Analysis (project leader)
    Multimedia Links Class Schedules

    Other Activities: Student Advising and Research Grants (Click Here)

    Contact information

    Main Contact:

    Telephone: 31-71-527-7034

    Fax: 31-71-527-6985

    Postal Address:

        Leiden Institute of Advanced Computer Science
        Leiden University
        Niels Bohrweg 1
        2333 CA Leiden
        The Netherlands
    


    Deep learning and multimedia retrieval

    Deep learning demo and publications


    Publications (technical reports, preliminary work)

    Content-based tag recommendation algorithms for unstructured data

    Improving SIFT accuracy with use of perspective transforms

    Improving the LSDh-tree for fast approximate nearest neighbor search

    Information-Synthesis Network for Facial Landmarks Estimation

    Preliminary Evaluation of CNN Classification by Objective Testers

    Sub-Image Search Engine

    Image Similarity Using Color Histograms

    Rating Inference


    PhD at Leiden University

    PhD at Leiden Guidelines

    PhD Training and Compulsory Courses

    ASCI Research School

    CSC and Leiden

    PhD Defense Rules and Guidelines - Cache


    Links

    Bus - Corpus

    Bus - Niels Bohrweg

    Bus - Universiteitsterrein

    Bus - De Kempenaerstraat

    Google.com - A good WWW search engine for general information and guides

    Google Search Operators

    Bing Search Operators (e.g. inbody:)

    Thesis Check - Turnitin

    IBM Watson Speech to Text

    Bus Corpus

    Bus Niels Bohrweg

    Dutch Basisverzekering

    Doctor Boender

    DBLP - Michael S. Lew

    DBLP - International Journal of Multimedia Information Retrieval

    MarBo Young on Writing

    Log out Amazon.com

    Log out Amazon.co.uk

    Archive.org - Way Back Machine (historical WWW)

    SAP Leiden

    LIM

    Waist to Height - Penn Health

    Sunblock Ingredients

    Random links

    Lily

    Campaign Promises - BBC

    Campaign Promises - USA Today

    Campaign Promises - Github

    Six Party Talks and Signed Declaration 2005

    GOPDND

    Covid live

    Dilbert

    Perldoc.com - Perl Documentation

    Multimedia Conferences 2011

    AMSR links

    Infinity Web Server

    ACM International Conference on Multimedia Retrieval

    ACM International Conference on Multimedia Retrieval, Glasgow, 2014 Web Archive

    ACM TOMCCAP MIR Survey (vol. 2, issue 1, pp. 1-19, 2006)

    Content-based tag recommendation algorithms for unstructured data

    Improving SIFT accuracy with use of perspective transforms

    Improving the LSDh-tree for fast approximate nearest neighbor search

    Information-Synthesis Network for Facial Landmarks Estimation

    Preliminary Evaluation of CNN Classification by Objective Testers

    Sub-Image Search Engine

    Image Similarity Using Color Histograms

    Rating Inference