LAMP

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.

See here current open positions.

Two NIPS papers got accepted. One work on ‘Image-to-image translation for cross-domain disentanglement’ (pdf) and one titled ‘Memory Replay GANs: Learning to Generate New Categories without Forgetting’ (pdf + blog + video).

Our research on transfering GANs has been accepted for ECCV 2018: ‘Transferring GANs: generating images from limited data‘.

Also our work on out of distribution detection has been accepted for BMVC 2018. Also see the project page.

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 and this BLOG POST. 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.

The papers ‘RankIQA: Learning from Rankings for No-reference Image Quality Assessment‘ and ‘Domain-adaptive deep network compression‘ have been accepted for ICCV, see here for a BLOG post on this research. . 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).