Master Thesis Defended

Congratulations to all our master students that defended their thesis last 16th of September during the 3rd Annual Catalan Meeting on Computer Vision!

  • Olaia Artieda Aguirre
  • Sergi Canyameres Masip
  • Xialei Liu
  • Arcadi Llanza Carmona
  • Guim Perernau Guirao (Best Thesis Award)

We also received the Best Poster Award for our poster based on the ‘Hierarchical Part Detection with Neural Networks‘.

WMT16 paper accepted

Our paper “Does Multimodality Help Human and Machine for Translation and Image Captioning?” has been accepted on the ACL 2016 First Conference on Machine Translation. Check some results here.

Business Track Winners

Two of our Ph.D. students, Laura López and Marc Masana, participated in the Accenture Digital Datathon. The competition consisted on generating a model for traffic accident prediction in the city of Barcelona. Our student’s team won the Business Track by unanimous decision of the jury.

] From left to right, the four team members (Andrej, Marc, Laura and David) and Lluis Puerto, the RACC Technical Director.

ICLR and ICIP accepted

Our paper ‘On-the-fly Network Pruning for Object Detection‘ has been accepted at ICLR 2016 on the Workshop Track.

Also, the paper ‘Hierarchical Part Detection with Deep Neural Networks‘ has been accepted at the ICIP 2016.

ICLR paper accepted

The work ‘Fitnets: Hints for thin Deep Nets’ will be presented at the ICLR 2015. The paper proposes a framework to compress wide and deep networks into thin and deep ones.

TIP on stereo estimation

Our paper ‘Accurate stereo matching by two-step energy minimization’ has been accepted in IEEE TIP (pdf). Sofware can be downloaded here and comparison on the Middlebury data set is vailable here.

Another PAMI accepted!

Our work on discriminative sparse reconstruction for ranking problems like person re-identification has accepted in PAMI. This paper shows how to extend sparse discriminative classifiers to ranking problems through iterative re-weighting of sparse solutions. Code is available here.

Welcome to the Learning and Machine Perception (LAMP) site.

The Learning and Machine Perception (LAMP) team at the Computer Vision Center conducts fundamental research and technology transfer in the field of machine learning for semantic understanding of visual data. The group works with a wide variety of visual data sources: from multispectral, medical imagery and consumer camera images, to live webcam streams and video data. The returning objective is the design of efficient and accurate algorithms for the automatic extraction of semantic information from visual media.

PAMI accepted

Our work on unsupervised learning of sparse features has been accepted in PAMI. The method aims at both lifetime and population sparsity in order to learn discriminative features. The code is available here and additional results are reported in this paper presented at WHISPERS.

New Facial Behavior Analysis paper

Our BMVC paper on facial behavior analysis has been accepted for oral presentation (project page).