Visual Intelligence and Learning Laboratory

who we are

We are a research group at the Swiss Federal Institute of Technology (EPFL)‘s School of Computer and Communication Sciences (IC). Our research focus is broadly on Computer Vision, Machine Learning, and Perception-for-Robotics.

our projects

Here you can find highlighted our most recent projects.

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

T. Yeo, O.F. Kar, Z. Sodagar, A. Zamir
ICCV, 2023.

Modality-invariant Visual Odometry for Embodied Navigation

M. Memmel, R. Bachmann, A. Zamir
CVPR, 2023.

Task Discovery: Finding the Tasks that Neural Networks Generalize on

A. Atanov, A. Filatov, T. Yeo, A. Sohmshetty, A. Zamir
NeurIPS, 2022.

PALMER: Perception-Action Loop with Memory Reorganization for Planning

T. Yeo, O.F. Kar, Z. Sodagar, A. Zamir
ICCV, 2023.

MultiMAE: Multi-modal Multi-task Masked Autoencoders

R. Bachmann*, D. Mizrahi*, A. Atanov, A. Zamir
ECCV, 2022.

3D Common Corruptions and Data Augmentation

O.F. Kar, T. Yeo, A. Atanov, A. Zamir
CVPR, 2022. [Oral]

CLIPasso: Semantically-Aware Object Sketching

Y. Vinker, E. Pajouheshgar, J. Y. Bo, R. Bachmann, A. H. Bermano, D. Cohen-Or, A. Zamir, A. Shamir
Transactions on Graphics (Proceedings of SIGGRAPH), 2022. [Best Paper Award]

Robustness via Cross-Domain Ensembles

T. Yeo*, O.F. Kar*, A. Zamir
ICCV, 2021. [Oral]

Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

A. Eftekhar*, A. Sax*, R. Bachmann, J. Malik, A. Zamir
ICCV, 2021.

Robust Learning Through Cross-Task Consistency

A. Zamir*, A. Sax*, T. Yeo, O. Kar, N. Cheerla, R. Suri, J. Cao, J. Malik, L. Guibas
CVPR, 2020. [Best Paper Award Nominee]

Which Tasks Should Be Learned Together in Multi-task Learning?

T. Standley, A. Zamir, D. Chen, L. Guibas, J. Malik, S. Savarese
ICML, 2020.

Side-tuning: Network Adaptation via Additive Side Networks

J. Zhang, A. Sax, A. Zamir, L. Guibas, J. Malik
ECCV, 2020. [Spotlight]

Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation

B. Chen, S. Sax, L. Pinto, F. Lewis, I. Armeni, S. Savarese, A. Zamir, J. Malik
CoRL, 2020.

Prospective Members

PhD Applicants: We are always looking for highly talented and motivated students. However, you don’t need to send an email first. PhD admissions at EPFL are centralized and competitive. Please directly apply to the PhD program of computer science (EDIC). The admission system allows you to identify the professors/labs you’re interested in. If you believe your case is a unique fit for our lab, please send an email with your CV as well, after you submit your application.

Postdocs: Please email Amir directly

BS/MS interns and project students: If you’re an EPFL student, send us an email with your CV attached. If you are not and you are eligible for Summer@EPFL internship program, please apply here. Otherwise, feel free to send us an email with your CV attached.