The DiCarlo Lab at MIT

Publications

2012
DiCarlo, J.
Zoccolan, Davide
Rust, C.
How Does the Brain Solve Visual Object Recognition?

Neuron

https://www.sciencedirect.com/science/article/pii/S089662731200092X

Google Scholar
Abstract: How Does the Brain Solve Visual Object Recognition?
2012
Majaj, Najib J
Hong, Ha
Solomon, EA
DiCarlo, James J.
A unified neuronal population code fully explains human object recognition.

Computation and Systems Neuroscience (COSYNE)

http://cosyne.org/cosyne12/Cosyne2012_program_book.pdf

Google Scholar
Abstract: A unified neuronal population code fully explains human object recognition.
2011
Pinto, Nicolas
Barhomi, Y
Cox, David D.
DiCarlo, James J.
Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks

IEEE Workshop on Applications of Computer Vision (WACV)

https://ieeexplore.ieee.org/document/5711540

Google Scholar
Abstract: Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks
2011
Pagan, Marino
Neissi, AA
Baldassi, Carlo
Zecchina, Riccardo
DiCarlo, James J.
Zoccolan, Davide
From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex

Computational and Systems Neuroscience (COSYNE)

http://www.cosyne.org/c/index.php?title=Cosyne_11_posters/I-89

Google Scholar
Abstract: From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex
2010
Rust, N. C.
DiCarlo, J. J.
Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT

Journal of Neuroscience

http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.0179-10.2010

Google Scholar
Abstract: Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT
2010
Li, Nuo
DiCarlo, James J.
Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex

Neuron

https://linkinghub.elsevier.com/retrieve/pii/S0896627310006392

Google Scholar
Abstract: Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex
2010
Aparicio, Paul L.
Issa, Elias B.
DiCarlo, James J.
What is the middle face patch?

Society for Neuroscience

https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=e08f5ff4-1ba9-4faf-a459-5c9d4be0a1bf&cKey=36fa0d7d-3e83-4910-be75-57d361ae9e58&mKey=%7bE5D5C83F-CE2D-4D71-9DD6-FC7231E090FB%7d

Google Scholar
Abstract: What is the middle face patch?
2010
Issa, Elias B.
Papanastassiou, A. M.
Andken, BB
DiCarlo, James J.
Towards large-scale, high resolution maps of object selectivity in inferior temporal cortex

Computation and Systems Neuroscience (COSYNE)

https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00154&eid=770&sname=computational_and_systems_neur

Google Scholar
Abstract: Towards large-scale, high resolution maps of object selectivity in inferior temporal cortex
2010
DiCarlo, James J.
Dickenson, SJ
Leonardis, A.
Schiele, B.
Tarr, MJ.
Do we have a strategy for understanding how the visual system accomplishes object recognition?

Object Categorization: Computer and Human Vision Perspectives

Google Scholar
Abstract: Do we have a strategy for understanding how the visual system accomplishes object recognition?
2010
Li, Nuo
DiCarlo, James J.
Does the visual system use natural experience to construct size invariant object representations?

Computation and Systems Neuroscience (COSYNE)

https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00326&eid=770&sname=Computational_and_Systems_Neuroscience_2010

Google Scholar
Abstract: Does the visual system use natural experience to construct size invariant object representations?
2010
Pinto, Nicolas
Majaj, Najib J
Barhomi, Y
Solomon, E. A.
Cox, David D.
DiCarlo, James J.
Human versus machine: comparing visual object recognition systems on a level playing field

Computation and Systems Neuroscience (COSYNE)

https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00283&eid=770&sname=Computational_and_Systems_Neuroscience_2010

Google Scholar
Abstract: Human versus machine: comparing visual object recognition systems on a level playing field
2010
Pinto, Nicolas
DiCarlo, James J.
Cox, David D.
A High-Throughput Screening Approach to Biologically-Inspired Object Recognition

Learning Workshop-Computation and Systems Neuroscience (COSYNE)

Google Scholar
Abstract: A High-Throughput Screening Approach to Biologically-Inspired Object Recognition
2009
Li, Nuo
Cox, David D.
Zoccolan, Davide
DiCarlo, James J.
What Response Properties Do Individual Neurons Need to Underlie Position and Clutter “Invariant” Object Recognition?

Journal of Neurophysiology

https://www.physiology.org/doi/10.1152/jn.90745.2008

Google Scholar
Abstract: What Response Properties Do Individual Neurons Need to Underlie Position and Clutter “Invariant” Object Recognition?

PDF
2009
Rust, N. C.
DiCarlo, J. J.
Balanced increases in selectivity and invariance produce constant sparseness across the ventral visual pathway

Journal of Vision

http://jov.arvojournals.org/Article.aspx?doi=10.1167/9.8.738

Google Scholar
Abstract: Balanced increases in selectivity and invariance produce constant sparseness across the ventral visual pathway
2009
Pinto, Nicolas
Doukhan, David
DiCarlo, James J.
Cox, David D.
Friston, Karl J.
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

PLoS Computational Biology

https://dx.plos.org/10.1371/journal.pcbi.1000579

Google Scholar
Abstract: A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

PDF

PDF
2009
Pinto, Nicolas
DiCarlo, James J.
Cox, David D.
How far can you get with a modern face recognition test set using only simple features?

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops)

https://ieeexplore.ieee.org/document/5206605

Google Scholar
Abstract: How far can you get with a modern face recognition test set using only simple features?

PDF
2009
Zoccolan, D.
Oertelt, N.
DiCarlo, J. J.
Cox, D. D.
A rodent model for the study of invariant visual object recognition

Proceedings of the National Academy of Sciences

http://www.pnas.org/cgi/doi/10.1073/pnas.0811583106

Google Scholar
Abstract: A rodent model for the study of invariant visual object recognition

PDF
2009
Li, Nuo
DiCarlo, James J.
The size invariance of neuronal object representations can be reshaped by temporally contiguous visual experience

Society for Neuroscience

https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=8bb461de-0fd1-4f6a-9dfe-d62b65382083&cKey=507938c9-dc2c-4a47-a74f-601df562eddc&mKey=%7b081F7976-E4CD-4F3D-A0AF-E8387992A658%7d

Google Scholar
Abstract: The size invariance of neuronal object representations can be reshaped by temporally contiguous visual experience
2009
Pinto, Nicolas
Cox, David D.
DiCarlo, James J.
Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach

NVIDIA GPU Technology Conference

Google Scholar
Abstract: Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach
2009
Pinto, Nicolas
Cox, David D.
DiCarlo, James J.
Unlocking Brain-Inspired Computer Vision

GPU@BU

Google Scholar
Abstract: Unlocking Brain-Inspired Computer Vision