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.

Lichao’s paper on visual tracking Learning the Model Update for Siamese Trackers and Hamed’s paper on active learning Active Learning for Deep Detection Neural Networks (+suppl. material) (github) have been accepted for ICCV. Also, David Berga has a paper with Xavier Otazu on saliency detection: SID4VAM: A Benchmark Dataset With Synthetic Images for Visual Attention Modeling . In addition we also have two workshop papers on Multi-Modal Fusion for End-to-End RGB-T Tracking and on Temporal Coherence for Active Learning in Videos.

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.