Postdoctoral fellow
Massachusetts Institute of Technology
Computational Vision and Artificial Intelligence
Note: I am looking for students to work on a project related to Computational Human Vision and Deep Learning. If you are interested, please send me and email to gemmar@mit.edu.
Bio
My research aim is to build computational models of human vision to understand its underlying principles, and to use those models to build applications of artificial intelligence.
I am currently a postdoctoral fellow at Massachusetts Institute of Technology in the Center for Brains Minds and Machines, with Prof. Tomaso Poggio as my faculty host. I am also affiliated at the Laboratory for Computational and Statistical Learning, which is a collaborative agreement between the Istituto Italiano di Tecnologia and the Massachusetts Institute of Technology.
I pursued my doctoral degree in Computer Vision at ETH Zurich. Previously, I was a research assistant at the Computer Vision Lab at EPFL in Lausanne, at the Department of Media Technologies at Ramon Llull University in Barcelona, and at the Robotics Institute - Carnegie Mellon University in Pittsburgh.
Publications
Google scholar profile
- Do Deep Neural Networks Suffer from Crowding?
- A. Volokitin, G. Roig, T. Poggio
- CBMM memo 069, arXiv:1706.08616, 2017
- [pdf][bib][code]
- Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision
- F. Chen, G. Roig, L. Isik, X. Boix and T. Poggio
- In AAAI Spring Symposium Series, Science of Intelligence, 2017
- [pdf][bib]
- Is the Human Visual System Invariant to Translation and Scale?
- Y. Han, G. Roig, G. Geiger and T. Poggio
- In AAAI Spring Symposium Series, Science of Intelligence, 2017
- [pdf][bib]
- Herding Generalizes Diverse M-Best Solutions
E. Ozcan, G. Roig, O. Goksel, X. Boix
In arxiv:1611.04353, 2016
[pdf][bib]
- Learning to Predict Sequences of Human Visual Fixations
- M. Jiang, X. Boix, G. Roig, J. Xu, L. Van Gool, and Q. Zhao
- In IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016
- [pdf][bib]
- Foveation-based Mechanisms Alleviate Adversarial Examples
- Y. Luo, X. Boix, G. Roig, T. Poggio, Q. Zhao
- CBMM memo 044, arXiv:1511.06292, 2015
- [pdf][bib]
- SEEDS: Superpixels Extracted via Energy-Driven Sampling
- Saliency Prediction with Active Semantic Segmentation
- M. Jiang, X. Boix, J. Xu, G. Roig, L. Van Gool, Q. Zhao
- In Proc. of British Machine Vision Conference (BMVC), 2015
- [pdf][bib]
- Self-Adaptable Templates for Feature Coding
- X. Boix*, G. Roig*, Salomon Diether, L. Van Gool
- In Proc. of Neural Information Processing Systems (NIPS), 2014
- (*equal contribution). [pdf][bib]
- Random Binary Mappings for Kernel Learning and Efficient SVM
- G. Roig*, X. Boix*, L. Van Gool
- In arXiv:1307.5161, 2014
- (*equal contribution). [pdf][bib]
-
- Comment on "Ensemble Projection for Semi-supervised Image Classification"
- X. Boix, G. Roig, L. Van Gool
- In arXiv:1408.6963, 2014
- [pdf][bib]
- Active MAP Inference in CRFs for Efficient Semantic Segmentation
- G. Roig*, X. Boix*, R. de Nijs, S. Ramos, K. Kuehnlenz, L. Van Gool
- In Proc. of International Conferenence on Computer Vision (ICCV), 2013
- (Oral, *equal contribution). [pdf][bib]
- Online Video SEEDS for Temporal Window Objectness
- M. Van den Bergh, G. Roig, X. Boix, S. Manen, L. Van Gool
- In Proc. of International Conferenence on Computer Vision (ICCV), 2013
- [pdf][bib][code][project]
- Sparse Quantization for Patch Description
- X. Boix, M. Gygli, G. Roig, L. Van Gool
- In Proc. of Computer Vision and Pattern Recognition (CVPR), 2013
- [pdf][bib]
- Nested Sparse Quantization for Efficient Feature Coding
- X. Boix*, G. Roig*, L. Van Gool
- In Proc. of European Conferenence on Computer Vision (ECCV), 2012
- (*equal contribution). [pdf][bib]
- SEEDS: Superpixels Extracted via Energy-Driven Sampling
- On-line Semantic Perception Using Uncertainty
- R. de Nijs, S. Ramos, G. Roig, X. Boix, L. Van Gool, K. Kühnlenz
- In Proc. of International Conference on Intelligent Robots and Systems (IROS), 2012
- [pdf][bib]
- Robotic ADaptation to Humans Adapting to Robots: Overview of the FP7 project RADHAR.
- E. Demeester, E. Vander Poorten, A. Hüntemann, J. De Schutter, B. Lau, M. Kuderer, W. Burgard, A. Fossati, G. Roig, X. Boix, M. Ristin, L. Van Gool, et al..
- In Proc. of 1st International Conference on Systems and Computer Science, 2012
- [pdf][bib][project]
- Conditional Random Fields for Multi-Camera Object Detection.
- G. Roig*, X. Boix*, H. Ben Shitrit, P. Fua.
- In Proc. of International Conferenence on Computer Vision (ICCV), 2011.
- (*equal contribution). [pdf][bib][project]
- Hierarchical CRF with Product Label Spaces for Parts-based Models.
- G. Roig*, X. Boix*, F. De la Torre, J. Serrat, C. Vilella.
- In Proc. of Face and Gesture (FG) 2011.
- (Oral, *equal contribution). [pdf][bib]
- Optimal Feature Selection for Subspace Image Matching.
- G. Roig, X. Boix, F. De la Torre.
- In Proc. of International Conference on Computer Vision Workshops (ICCVW), 2009.
- [pdf][bib]
INTERNATIONAL WORKSHOPS AND CONFERENCE ABSTRACTS
- Eccentricity Dependent Deep Neural Networks for Modeling Human Vision.
G. Roig, F. Chen, X. Boix and T. Poggio.
Vision Sciences Society, 2017.
(Abstract + poster).
- On the Human Visual System Invariance to Translation and Scale.
- Y. Han, G. Roig, G. Gaiger and T. Poggio.
- Vision Sciences Society, 2017.
(Abstract + poster).
- Online Video SEEDS for Temporal Window Objectness.
- M. Van den Bergh, G. Roig, X. Boix, S. Manen, L. Van Gool.
- In European Conferenence on Computer Vision (ECCVW), 2013.
- (Talk + poster).
- Ensemble Kernel Learning.
- G. Roig*, X. Boix*, C. Leistner, L. Van Gool.
- In International Conferenence on Computer Vision Workshops (ICCVW), 2011.
- (Abstract + poster).
Contact
Gemma Roig, PhD
gemmar@mit.edu
Center for Brains Minds and Machines
Poggio Lab
Laboratory for Computational and Statistical Learning - IIT@MIT
Massachusetts Institute of Technology
Dept. of Brain and Cognitive Sciences
Bldg. 46-5155
77 Massachusetts Avenue
Cambridge, MA 02139