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Deep learning post-doc position

We are seeking a postdoc to join the Learning and Machine Perception (LAMP) team in beautiful Barcelona. The position is for 18 months years (could be extended by an additional year). Applications are welcome until 15th Sept. The starting date should be October/November 2021.

Joost Van de Weijer will be the supervisor. He is the leader of the Learning and Machine Perception (LAMP) team at the CVC. LAMP is in the Computer Vision Center at the Universitat Autònoma de Barcelona. The position is a sponsored industrial project with 2 postdocs and four PhD students. The subjects include lifelong learning (continual learning), generative models, reinforcement learning, and domain adaptation. For more information on the LAMP team visit:

The candidate should possess a PhD in computer vision or machine learning, and have a strong publication record. We are looking for candidates who have publications in the top conferences CVPR, ECCV, ICCV, NIPS, ICML, or ICLR. The candidate should have a background in machine learning and deep learning. The applicants are expected to be fluent in both oral and written communication in English. We are interested in also including reinforcement learning in the project and encourage candidates with experience in this field to apply. We are looking for good team players, that demonstrate initiative and independence. The candidate is expected to supervise PhD students.

The selected candidate will work in the Computer Vision Centre (CVC), Barcelona, a research institute comprising more than 100 researchers and support staff, dedicated to artificial intelligence and computer vision research. With a strong international projection and links to the industry, the Computer Vision Centre offers an exciting environment for scientific career development. The Computer Vision Centre has a plan for expansion of its permanent research staff base, and has received the “HR Excellence in Research” award as a provider and supporter of a stimulating and favorable working environment.

If you are interested in the position, please contact Dr. Joost van de Weijer for more information and applications (jo…

Living allowance of 30-40k€/year (gross salary) depending on experience

Two papers at ICCV2021

Two papers have been accepted for the main track: Yaxing’s paper on TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets. And Shiqi’s paper on Generalized Source-free Domain Adaptation (see project page).

CVPR 2021

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

Also four CVPR workshop papers have been accepted:

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.

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.

Schedule 2019

Tentative schedule:
July 4: Lu

June 13: Marc will present ‘Large Scale Incremental Learning’ (CVPR2019)

June 6: Mikel presents FearNet: Brain-Inspired Model for Incremental Learning

May 10: Lichao presents Learning Discriminative Model Prediction for Tracking
May 2: Kai presents his work and
Fei presents:
Efficient Variable Rate Image Compression with Multi-scale Decomposition Network, Trans. on CSVT, 2019

Apr 9: Marc and Lu present their own work.

Apr 4: Kai wil present
Hardness-Aware Deep Metric Learning (CVPR2019 oral)

Oguz presents his work.

Mar 28: Lichao will present his work.

Carola will present:
Large Scale Fine-Grained Categorization and Domain-Specific Transfer
(CVPR 2018)

Mar 7: Chenshen will present:
The lottery ticket hypothesis: finding sparse, trainable neural networks
(ICLR 2019 Oral)

Feb 28: Xialei will present:
On the Sensitivity of Adversarial Robustness to Input Data Distributions (ICLR 2019)

Oguz will present:
Semantic Regularisation for Recurrent Image Annotation

Feb 14: Yaxing will present:
Unsupervised Learning of Object Landmarks through Conditional Image Generation

Javad will present:
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles

Thesis Aymen Azaza

Aymen Azaza has been awarded his PhD both at the UAB and the University of Monastir (Tunisie) for his thesis on ‘Context, Motion, and Semantic Information for Computational Saliency’.

TIP accepted on RGB-D

The journal on ‘Learning Effective RGB-D Representations for Scene Recognition’ got accepted for IEEE TIP. See here for a blog post on this paper.


Dec 12 Lichao will present his recent work.

Kai will present Robust Classification with Convolutional Prototype Learning (CVPR2018)

Nov 28 Lu will present the paper: Learning to Compare: Relation Network for Few-Shot Learning.

Yaxing will present his recent research.

Nov 7: Carola will present Associating Inter-image Salient Instances for Weakly Supervised Semantic Segmentation (ECCV 2018)

Bogdan will present Progress & Compress: A scalable framework for continual learning (ICLR 2018)

Oct 31: Chenshen will present Large Scale GAN Training for High Fidelity Natural Image Synthesis

Oct 17: Oguz will present Deep Randomized Ensembles for Metric Learning (ECCV 2018)

Abel will present What do I Annotate Next? An Empirical Study of Active Learning for Action Localization

Oct 10: Marc presents Memory Aware Synapses: Learning what (not) to forget

Sept 19: Fei presents DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency (ECCV 2018)

Laura presents: Group Normalization (ECCV 2018)

Sept 5:Xialei presents Deep Matching Autoencoders (CVPR 2018)