Hossein Azizpour

Ph.D. Student
Royal Institute of Technology (KTH)
School of Computer Science
and Communications

Department of Computational Vision
and Active Perception (CVAP)

Stockholm, SE 100-44, Sweden

Room 618, Teknikringen 14, floor 6, SE-114 28 Stockholm, Sweden
+46 (8) 790 6353

Short Bio. I am currently a Ph.D. student! I have the honor of doing research under the supervision of Prof. Stefan Carlsson. I am also co-supervised by Dr. Josephine Sullivan. During some periods in 2010 and 2011 I was lucky to have the opportunity of working with Dr. Ivan Laptev at INRIA-WILLOW project located in Paris!. I've got a M.Sc. degree in System, Control and Robotics from KTH School of Electrial Engineering and B.Sc. from Amirkabir University of Technology (Tehran Polytechnic) in Software Engineering!. I was born in Mashhad, Iran. My hobbies are travelling, discussion!, watching movies, reading books, and taking programming contests. Every now and then I've been asked to act as a reviewer! for IEEE transactions on Pattern Recognition and Machine Intelligence (TPAMI), Transactions on Image Processing (TIP), Transactions on Neural Networks (TNNLS), CVPR, ICCV, BMVC.


My current research efforts are in the area of computer vision. I intend to use machine learning and graphs theory machineries for applications in computer vision. In general, I am interested in feature selection and sharing for various visual recognition tasks. In particular, my main focus during Ph.D. is on Deformable Part Models and Deep Learning.


Factors of Transferability for a Generic ConvNet Representation
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson
in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)   
Materials: PDF
Spotlight the Negatives: A Generalized Discriminative Latent Model
Hossein Azizpour, Mostafa Arefiyan, Sobhan Naderi Parizi, Stefan Carlsson
in BMVC 2015, Swansea, UK   
Materials: PDF Poster PDF
From Generic to Specific Deep Representation for Visual Recognition [Best Paper Award]
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson
in CVPRW 2015, DeepVision workshop, Boston, US   
Materials: PDF
Persistent Evidence of Local Image Properties in Generic ConvNets
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Atsuto Maki, Carl Henrik Ek, Stefan Carlsson
in SCIA 2015, Copenhagen, Denmark   
Materials: PDF
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
Hossein Azizpour, Stefan Carlsson   
arXiv pre-print

Materials: PDF
CNN Features off-the-shelf: An Astounding Baseline for Recognition [Best Paper Runner-up Award]
Ali S Razavian Hossein Azizpour, Josephine Sullivan, Stefan Carlsson   
in CVPRW 2014, DeepVision workshop, Columbus, US   
Materials: PDF   Project Page     arXiv Tech Report  
Multi-view Body Part Recognition with Random Forests   [Best Industry Paper Award]
Vahid Kazemi, Magnus Burenius, Hossein Azizpour, Josephine Sullivan,   
in BMVC 2013, Bristol, UK
Materials:   Project and Data   PDF
Object Detection Using Strongly-Supervised Deformable Part Models
Hossein Azizpour, Ivan Laptev.   
in ECCV 2012, Florence, Italy
Materials:   Project and Data   Code    PDF
Mixture Component Identification and Learning for Visual Recognition
Omid Aghazadeh, Hossein Azizpour, Josephine Sullivan, Stefan Carlsson.
in ECCV 2012, Florence, Italy
Materials: PDF

Selected Awards and Honors

  • Best Paper Award from DeepVision workshop in CVPR 2015
  • Google Travel Grant for CVPR 2015 DeepVision workshop
  • Best Paper Runner-Up Award from DeepVision workshop in CVPR 2014
  • Best Industry Paper Prize from BMVC 2013
  • Outstanding reviewers' award for CVPR 2013
  • Granted Scholarship at KTH for an amount of 50000 SEK in 2010
  • Two times ACM ICPC World Finalist 2007 in Tokyo, Japan and 2008 in Banff, Canada

Some activities and hobbies

WARNING: The information published herein (except for the codes) is for the personal use of the reader. Copyright and all rights therein are retained by authors or by other copyright holders. Making copies of the information in full or any portion for purposes other than own use is a violation of copyright law.