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.

4 CVPR’s accepted !

Members of LAMP got four accepted CVPR 2018 papers. The papers ‘Mix and match networks: encoder-decoder alignment for zero-pair imagetranslation‘ has been accepted, for more information see the project page. Xialei has a paper on crowd counting: ‘Leveraging Unlabeled Data for Crowd Counting by Learning to Rank‘ or see the project page.

Also Aitor Alvarez got his paper ‘On the Duality Between Retinex and Image Dehazing‘ accepted in collaboration with Adrian Galdran from INESC TEC Porto, Portugal and Javier Vazquez-Corral, Marcelo Bertalmıo from Universitat Pompeu Fabra. Also Abel Gonzalez got his paper ‘Objects as context for detecting their semantic parts’. The research was performed in his previous group CALVIN with Vitorrio Ferrari.

2 ICCV papers accepted

The papers ‘RankIQA: Learning from Rankings for No-reference Image Quality Assessment‘ and ‘Domain-adaptive deep network compression‘ have been accepted for ICCV. Papers and project pages will be soon available. We also have a paper on ‘Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB‘ in the workshop on Physics Based Vision meets Deep Learning (PBDL)

2 ICPR papers accepted

The papers ‘Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting‘ and ‘Weakly Supervised Domain-Specific Color Naming Based on Attention’ have been accepted for publication at ICPR 2018 in Beijing. Final version and project pages will be published soon.

Convolutional Neural Networks for Texture Recognition and Remote Sensing

Our work on ‘Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification‘ has been accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing.

World Mobile Congress

The presentation at the world mobile congress on the CVPR paper on crowd counting received quite some press coverage.

Journal on Color Names

In this recent work we investigate how to extend the number of color names beyond the standard set of eleven. The work ‘Beyond Eleven Color Names for Image Understanding‘ is published in Machine Vision and Applications. For more information visit the project website. We also published a workshop paper at WorldCIST conference on color naming for multi-color fashion items.

New postdoc

We are happy to have Abel Gonzalez Garcia join the group as a postdoc.

Review on Computer Vision Techniques in Emergency Situations

Our review article on Computer Vision Techniques in Emergency Situations got accepted in Multimedia Tools and Applications.

Multimodal Translation Challenge

We obtained first rank on the WMT17 Shared Task on Multimodal Translation in our submission within the M2CR project together with the Le Mans natural language processing group. The system is explained in this WMT2017 paper ‘LIUM-CVC Submissions for WMT17 Multimodal Translation Task‘.