@article {171, title = {Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness}, journal = {bioRxiv}, year = {2022}, month = {July 4, 20202}, type = {preprint}, abstract = {

While some state-of-the-art artificial neural network systems in computer vision are strikingly accurate models of the corresponding primate visual processing, there are still many discrepancies between these models and the behavior of primates on object recognition tasks. Many current models suffer from extreme sensitivity to adversarial attacks and often do not align well with the image-by-image behavioral error patterns observed in humans. Previous research has provided strong evidence that primate object recognition behavior can be very accurately predicted by neural population activity in the inferior temporal (IT) cortex, a brain area in the late stages of the visual processing hierarchy. Therefore, here we directly test whether making the late stage representations of models more similar to that of macaque IT produces new models that exhibit more robust, primate-like behavior. We conducted chronic, large-scale multi-electrode recordings across the IT cortex in six non-human primates (rhesus macaques). We then use these data to fine-tune (end-to-end) the model \“IT\” representations such that they are more aligned with the biological IT representations, while preserving accuracy on object recognition tasks. We generate a cohort of models with a range of IT similarity scores validated on held-out animals across two image sets with distinct statistics. Across a battery of optimization conditions, we observed a strong correlation between the models\’ IT-likeness and alignment with human behavior, as well as an increase in its adversarial robustness. We further assessed the limitations of this approach and find that the improvements in behavioral alignment and adversarial robustness generalize across different image statistics, but not to object categories outside of those covered in our IT training set. Taken together, our results demonstrate that building models that are more aligned with the primate brain leads to more robust and human-like behavior, and call for larger neural data-sets to further augment these gains.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {https://doi.org/10.1101/2022.07.01.498495}, url = {https://www.biorxiv.org/content/10.1101/2022.07.01.498495v1.full.pdf}, author = {Dapello, Joel and Kar, Kohitij and Schrimpf, Martin and Geary, Robert and Ferguson, Michael and Cox, David D. and DiCarlo, James J.} } @proceedings {43, title = {Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks}, journal = {IEEE Workshop on Applications of Computer Vision (WACV)}, year = {2011}, month = {01/2011}, pages = {463-470}, publisher = {IEEE}, address = {Kona, Hawaii, USA}, abstract = {

Tolerance (\“invariance\”) to identity-preserving image variation (e.g. variation in position, scale, pose, illumination) is a fundamental problem that any visual object recognition system, biological or engineered, must solve. While standard natural image database benchmarks are useful for guiding progress in computer vision, they can fail to probe the ability of a recognition system to solve the invariance problem. Thus, to understand which computational approaches are making progress on solving the invariance problem, we compared and contrasted a variety of state-of-the-art visual representations using synthetic recognition tasks designed to systematically probe invariance. We successfully re-implemented a variety of state-of-the-art visual representations and confirmed their published performance on a natural image benchmark. We here report that most of these representations perform poorly on invariant recognition, but that one representation shows significant performance gains over two baseline representations. We also show how this approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition.

}, issn = {978-1-4244-9497-2}, doi = {10.1109/WACV.2011.5711540}, url = {https://ieeexplore.ieee.org/document/5711540}, author = {Pinto, Nicolas and Barhomi, Y and Cox, David D. and DiCarlo, James J.} } @proceedings {52, title = {A High-Throughput Screening Approach to Biologically-Inspired Object Recognition}, journal = {Learning Workshop-Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {03/2010}, address = {Snow Bird, Utah, USA}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @proceedings {51, title = {Human versus machine: comparing visual object recognition systems on a level playing field}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {02/2010}, address = {Salt Lake City, Utah, USA}, abstract = {

It is received wisdom that biological visual systems easily outmatch current artificial systems at complex visual tasks like object recognition. But have the appropriate comparisons been made? Because artificial systems are improving every day, they may surpass human performance some day. We must understand our progress toward reaching that day, because that success is one of several necessary requirements for \"understanding\" visual object recognition. How large (or small) is the difference in performance between current state-of-the-art object recognition systems and the primate visual system? In practice, the performance comparison of any two object recognition systems requires a focus on the computational crux of the problem and sets of images that engage it. Although it is widely believed that tolerance (\"invariance\") to identity-preserving image variation (e.g. variation in object position, scale, pose, illumination) is critical, systematic comparisons of state-of-the-art artificial visual representations almost always rely on \"natural\" image databases that can fail to probe the ability of a recognition system to solve the invariance problem [Pinto et al PLoS08, COSYNE08, ECCV08, CVPR09]. Thus, to understand how well current state-of-the-art visual representations perform relative to each other, relative to low-level neuronal representations (e.g. retinal-like and V1-like), and relative to high-level representations (e.g. human performance), we tested all of these representations on a common set of visual object recognition tasks that directly engage the invariance problem. Specifically, we used a synthetic testing approach that allows direct engagement of the invariance problem, as well as knowledge and control of all the key parameters that make object recognition challenging. We successfully re-implemented a variety of state-of-the-art visual representations, and we confirmed the high published performance of all of these state-of-the-art representations on large, complex \"natural\â\€ image benchmarks. Surprisingly, we found that most of these representations were weak on our simple synthetic tests of invariant recognition, and only high-level biologically-inspired representations showed performance gains above the neuroscience \"null\" representation (V1-like). While in aggregate, we found that the performance of these state-of-the-art representations pales in comparison to human performance, humans and computers seem to fail in different and potentially enlightening ways when faced with the problem of invariance. We also show how our synthetic testing approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition.

}, doi = {10.3389/conf.fnins.2010.03.00283}, url = {https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00283\&eid=770\&sname=Computational_and_Systems_Neuroscience_2010}, author = {Pinto, Nicolas and Majaj, Najib J and Barhomi, Y and Solomon, E. A. and Cox, David D. and DiCarlo, James J.} } @article {56, title = {A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation}, journal = {PLoS Computational Biology}, volume = {5}, year = {2009}, month = {2009}, pages = {e1000579}, abstract = {

While many models of biological object recognition share a common set of \"broad-stroke\" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model\–e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct \"parts\" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end {NVIDIA} graphic cards and the {PlayStation} 3\&$\#$39;s {IBM} Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.

}, doi = {10.1371/journal.pcbi.1000579}, url = {https://dx.plos.org/10.1371/journal.pcbi.1000579}, author = {Pinto, Nicolas and Doukhan, David and DiCarlo, James J. and Cox, David D.}, editor = {Friston, Karl J.} } @conference {57, title = {How far can you get with a modern face recognition test set using only simple features?}, booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops)}, year = {2009}, month = {06/2009}, publisher = {IEEE}, organization = {IEEE}, address = {Miami, FL}, abstract = {

In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance \“bar\” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields \“state-of-the-art\” performance levels on a standard face recognition set (\“labeled faces in the wild\”). However, at the same time, even with the inclusion of MKL techniques, systems based on these simple features still fail on a synthetic face recognition test that includes more \“realistic\” view variation by design. These results underscore the importance of building test sets focussed on capturing the central computational challenges of real-world face recognition.

}, isbn = {978-1-4244-3992-8}, doi = {10.1109/CVPR.2009.5206605}, url = {https://ieeexplore.ieee.org/document/5206605}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @proceedings {60, title = {Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach}, journal = {NVIDIA GPU Technology Conference}, year = {2009}, month = {2009}, author = {Pinto, Nicolas and Cox, David D. and DiCarlo, James J.} } @proceedings {61, title = {Unlocking Brain-Inspired Computer Vision}, journal = {GPU@BU}, year = {2009}, month = {2009}, author = {Pinto, Nicolas and Cox, David D. and DiCarlo, James J.} } @proceedings {62, title = {The Visual Cortex and GPUs}, journal = {GPU Computing for Biomedical Research}, year = {2009}, month = {2009}, address = {Massachusetts General Hospital, Boston, MA}, author = {Pinto, Nicolas and Cox, David D. and DiCarlo, James J.} } @article {53, title = {What Response Properties Do Individual Neurons Need to Underlie Position and Clutter {\textquotedblleft}Invariant{\textquotedblright} Object Recognition?}, journal = {Journal of Neurophysiology}, volume = {102}, year = {2009}, month = {01/2009}, pages = {360 - 376}, abstract = {

Primates can easily identify visual objects over large changes in retinal position\–a property commonly referred to as position \"invariance.\" This ability is widely assumed to depend on neurons in inferior temporal cortex {(IT)} that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most {IT} neurons\&$\#$39; responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of {IT} population responses, by themselves, can support object recognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron\&$\#$39;s rank-order object preference under identity-preserving image transformations (e.g., clutter). Because {IT} neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream.

}, issn = {0022-3077}, doi = {10.1152/jn.90745.2008}, url = {https://www.physiology.org/doi/10.1152/jn.90745.2008}, author = {Li, Nuo and Cox, David D. and Zoccolan, Davide and DiCarlo, James J.} } @conference {70, title = {Establishing Good Benchmarks and Baselines for Face Recognition}, booktitle = {European Conference on Computer Vision-Faces in {\textquoteright}Real-Life{\textquoteright} Images Workshop}, year = {2008}, month = {10/2008}, publisher = {EECV}, organization = {EECV}, address = {Marseille, France}, abstract = {

Progress in face recognition relies critically on the creation of test sets against which the performance of various approaches can be evaluated. A good set must capture the essential elements of what makes the problem hard, while conforming to practical scale limitations. However, these goals are often deceptively difficult to achieve. In the related area of object recognition, Pinto et al. [2] demonstrated the potential dangers of using a large, uncontrolled natural image set, showing that an extremely rudimentary vision system (inspired by the early stages of visual processing in the brain) was able to perform on par with many state-of-the-art vision systems on the popular Caltech101 object set [3].\  At the same time, this same rudimentary system was easily defeated by an ostensibly \"simpler\" synthetic recognition test designed to better span the range of real world variation in object pose, position, scale, etc. These results suggested that image sets that look \"natural\" to human observers may nonetheless fail to properly embody the problem of interest, and that care must be taken to establish baselines against which performance can be judged. Here, we repeat this approach for the \"LabeledFaces in the Wild\" (LFW) dataset [1], and for a collection of standard face recognition tests. The goal of the present work is not to compete in the LFW challenge, per se, but to provide a baseline against which the performance of other systems can be judged. In particular, we found that our rudimentary \"baseline\" vision system was able to achieve 68\% correct performance on the LFW challenge, substantially higher than a pure chance\" baseline. We argue that this value might serve as a more useful baseline against which to evaluate absolute performance and argue that the LFW set, while perhaps not perfect, represents an improvement over other standard face sets.

}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @article {64, title = {High-Resolution Three-Dimensional Microelectrode Brain Mapping Using Stereo Microfocal X-ray Imaging}, journal = {Journal of Neurophysiology}, volume = {100}, year = {2008}, month = {01/2008}, pages = {2966 - 2976}, abstract = {

Much of our knowledge of brain function has been gleaned from studies using microelectrodes to characterize the response properties of individual neurons in vivo. However, because it is difficult to accurately determine the location of a microelectrode tip within the brain, it is impossible to systematically map the fine three-dimensional spatial organization of many brain areas, especially in deep structures. Here, we present a practical method based on digital stereo microfocal X-ray imaging that makes it possible to estimate the three-dimensional position of each and every microelectrode recording site in \"real time\" during experimental sessions. We determined the system\&$\#$39;s ex vivo localization accuracy to be better than 50 microm, and we show how we have used this method to coregister hundreds of deep-brain microelectrode recordings in monkeys to a common frame of reference with median error of {\textless}150 microm. We further show how we can coregister those sites with magnetic resonance images {(MRIs),} allowing for comparison with anatomy, and laying the groundwork for more detailed electrophysiology/functional {MRI} comparison. Minimally, this method allows one to marry the single-cell specificity of microelectrode recording with the spatial mapping abilities of imaging techniques; furthermore, it has the potential of yielding fundamentally new kinds of high-resolution maps of brain function.

}, issn = {0022-3077}, doi = {10.1152/jn.90672.2008}, url = {https://www.physiology.org/doi/10.1152/jn.90672.2008}, author = {Cox, David D. and Papanastassiou, Alexander M. and Oreper, Daniel and Andken, Benjamin B. and DiCarlo, James J.} } @proceedings {71, title = {A high-throughput screening approach to discovering good forms of visual representation}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, address = {Salt Lake City, Utah, USA}, author = {Cox, David D. and Pinto, Nicolas and Doukhan, David and Corda, B and DiCarlo, James J.} } @proceedings {73, title = {Is the rodent a valuable model system for studying invariant object recognition?}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, abstract = {

Despite the many advantages rodents offer in terms of experimental accessibility, they have never been extensively used as models to investigate the neuronal processing of visual objects. A crucial step in establishing whether rodents are suitable models for the study of object vision, is to assess if they are capable of invariant object recognition \– i.e., recognition of visual objects across the range of transformations that they typically undergo during natural vision (e.g., changes in position and size). In this study, we tested rats (Long-Evans) capability to discriminate between two geometrical shapes (square and triangle) presented at different sizes, positions and orientations. In each trial, a single shape was presented on a computer monitor and the animal had to report its identity. The experiment consisted of three phases. In phase I, each animal learned to discriminate between the two shapes presented at fixed size (40\° of visual angle), position (center of the monitor), and orientation. Na\ïve rats typically achieved \> 70\% correct performance in 2-3 weeks of training. In phase II, the animals were trained to perform the task while either the size, the horizontal position, or the orientation of the shapes was separately varied. Rats readily acquired this task (\> 70\% correct performance) across sizes ranging from 40\° to 10\°, positions spanning \±12\°, and orientations spanning \±40\°. During phase III of the experiment, we asked if rats could generalize to novel combinations of size, position, and orientation (a total of 100 transformations of each target shape were tested). Performance was typically \> 70\% correct for nearly all of these previously unseen transformations (performance of one rat over a subset of the tested conditions is shown in the figure) and even for a fraction of transformations for which feedback was withheld. These results show that rats can readily learn to: 1) discriminate between simple visual shapes; 2) disregard variations in their position, size and orientation; and 3) generalize to novel views. This suggests that the rat visual system contains the fundamental mechanisms that support object recognition. Therefore, given the broad ranges of experimental approaches available in rats, they may be a powerful new model system to study the neuronal basis of invariant object recognition

}, url = {http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf}, author = {Zoccolan, Davide and Cox, David D. and Oertelt, Nadja and Radwam, Basma and Tsang, Sabrina and DiCarlo, James J.} } @proceedings {77, title = {Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, author = {Pinto, Nicolas and Cox, David D. and Corda, B and Doukhan, David and DiCarlo, James J.} } @article {79, title = {Untangling invariant object recognition}, journal = {Trends in Cognitive Sciences}, volume = {11}, year = {2007}, month = {01/2007}, pages = {333 - 341}, abstract = {

Despite tremendous variation in the appearance of visual objects, primates can recognize a multitude of objects, each in a fraction of a second, with no apparent effort. However, the brain mechanisms that enable this fundamental ability are not understood. Drawing on ideas from neurophysiology and computation, we present a graphical perspective on the key computational challenges of object recognition, and argue that the format of neuronal population representation and a property that we term \&$\#$39;object tangling\&$\#$39; are central. We use this perspective to show that the primate ventral visual processing stream achieves a particularly effective solution in which single-neuron invariance is not the goal. Finally, we speculate on the key neuronal mechanisms that could enable this solution, which, if understood, would have far-reaching implications for cognitive neuroscience.

}, issn = {13646613}, doi = {10.1016/j.tics.2007.06.010}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1364661307001593}, author = {DiCarlo, James J. and Cox, David D.} } @proceedings {85, title = {Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance}, journal = {Society for Neuroscience}, year = {2006}, month = {10/2006}, publisher = {SFN}, address = {Atlanta, GA, USA}, author = {Li, N. and Cox, David D. and Zoccolan, Davide and DiCarlo, James J.} } @proceedings {91, title = {Is the {\textquotedblleft}binding problem{\textquotedblright} a problem in inferiotemporal cortex?}, journal = {Society for Neuroscience}, year = {2005}, month = {11/2005}, publisher = {SFN}, address = {Washington, DC, USA}, author = {Cox, David D. and DiCarlo, James J.} } @article {87, title = {Multiple Object Response Normalization in Monkey Inferotemporal Cortex}, journal = {Journal of Neuroscience}, volume = {25}, year = {2005}, month = {07/2005}, pages = {8150 - 8164}, abstract = {

The highest stages of the visual ventral pathway are commonly assumed to provide robust representation of object identity by disregarding confounding factors such as object position, size, illumination, and the presence of other objects (clutter). However, whereas neuronal responses in monkey inferotemporal cortex (IT) can show robust tolerance to position and size changes, previous work shows that responses to preferred objects are usually reduced by the presence of nonpreferred objects. More broadly, we do not yet understand multiple object representation in IT. In this study, we systematically examined IT responses to pairs and triplets of objects in three passively viewing monkeys across a broad range of object effectiveness. We found that, at least under these limited clutter conditions, a large fraction of the response of each IT neuron to multiple objects is reliably predicted as the average of its responses to the constituent objects in isolation. That is, multiple object responses depend primarily on the relative effectiveness of the constituent objects, regardless of object identity. This average effect becomes virtually perfect when populations of IT neurons are pooled. Furthermore, the average effect cannot simply be explained by attentional shifts but behaves as a primarily feedforward response property. Together, our observations are most consistent with mechanistic models in which IT neuronal outputs are normalized by summed synaptic drive into IT or spiking activity within IT and suggest that normalization mechanisms previously revealed at earlier visual areas are operating throughout the ventral visual stream.

}, keywords = {Animals, Brain Mapping, Macaca mulatta, Male, Photic Stimulation, Posture, Psychology, Recognition, Temporal Lobe, Visual Pathways, Visual Perception}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.2058-05.2005}, url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.2058-05.2005}, author = {Zoccolan, D. and Cox, David D. and DiCarlo, James J.} } @proceedings {92, title = {Multiple object response normalization in monkey inferotemporal cortex}, journal = {Society for Neuroscience}, year = {2005}, month = {11/2005}, publisher = {SFN}, address = {Washington, DC, USA}, author = {Zoccolan, Davide and Cox, David D. and DiCarlo, James J.} } @proceedings {95, title = {The effect of visual experience on the position tolerance of primate object representations}, journal = {Society for Neuroscience}, year = {2004}, month = {10/2004}, publisher = {SFN}, address = {San Diego, CA, USA}, author = {Cox, David D. and DiCarlo, James J.} }