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.

See here current open positions.

CVPR 2021

Fei’s paper Slimmable compressive autoencoders for practical neural image compression has been accepted for CVPR.

Two papers at NeurIPS 2020

Riccardo’s paper >RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning on continual learning of captioning systems, and Yaxing’s paper on transfer learning for image-to-image systems:DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs have been accepted !

4 papers at CVPR 2020

Four CVPR papers have been accepted:

and one workshop paper:

3 papers at ICCV2019

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 (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 .

BLOG posts:

MeRGANs: generating images without forgetting (NIPS 2018 + video)
Mix and match networks (CVPR 2018)
Rotating networks to prevent catastrophic forgetting (ICPR 2018)
Deep network compression and adaptation (ICCV2017)
Learning RGB-D features for images and videos (AAAI 2017, TIP 2018)

CVPR 2019

Our paper on
Learning Metrics from Teachers: Compact Networks for Image Embedding has been accepted for presentation at CVPR 2019.

2 NIPS 2018 accepted

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).

Three Continual Learning workshop papers

We have the following workshop papers at ICML:

And one at ECCV:

IJCV paper on multi-modal I2I

Yaxing’s paper on ‘Mix and match networks: cross-modal alignment for zero-pair image-to-image translation’ has been accepted for publication in IJCV.

Winner VOT-RGBT:

Lichao Zhang won the VOT-RGBT challenge this year. His work is published in the VOT 2019 workshop:
Multi-Modal Fusion for End-to-End RGB-T Tracking.

PR on fine-grained object detection

The journal of Carola on ‘Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data’ (pdf) has been accepted for publication in Pattern Recognition.