@article {3871,
	title = {Invariant Recognition Shapes Neural Representations of Visual Input},
	journal = {Annual Review of Vision Science},
	volume = {4},
	year = {2018},
	month = {10/2018},
	pages = {403 - 422},
	abstract = {<p>Recognizing the people, objects, and actions in the world around us is a crucial aspect of human perception that allows us to plan and act in our environment. Remarkably, our proficiency in recognizing semantic categories from visual input is unhindered by transformations that substantially alter their appearance (e.g., changes in lighting or position). The ability to generalize across these complex transformations is a hallmark of human visual intelligence, which has been the focus of wide-ranging investigation in systems and computational neuroscience. However, while the neural machinery of human visual perception has been thoroughly described, the computational principles dictating its functioning remain unknown. Here, we review recent results in brain imaging, neurophysiology, and computational neuroscience in support of the hypothesis that the ability to support the invariant recognition of semantic entities in the visual world shapes which neural representations of sensory input are computed by human visual cortex.</p>
},
	keywords = {computational neuroscience, Invariance, neural decoding, visual representations},
	issn = {2374-4642},
	doi = {10.1146/annurev-vision-091517-034103},
	url = {https://www.annualreviews.org/doi/10.1146/annurev-vision-091517-034103},
	author = {Andrea Tacchetti and Leyla Isik and Tomaso Poggio}
}
@article {1760,
	title = {Atoms of recognition in human and computer vision},
	journal = {PNAS },
	volume = {113},
	year = {2016},
	month = {03/2016},
	pages = {2744{\textendash}2749},
	abstract = {<div>Discovering the visual features and representations used by thebrain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkableprogress and have begun to rival human performance in some challenging tasks. These models are trained on image examples andlearn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models aresimilar to those used by the human visual system. Here we show,by introducing and using minimal recognizable images, that thehuman visual system uses features and processes that are not usedby current models and that are critical for recognition. We found bypsychophysical studies that at the level of minimal recognizableimages a minute change in the image can have a drastic effect onrecognition, thus identifying features that are critical for the task.Simulations then showed that current models cannot explain thissensitivity to precise feature configurations and, more generally,do not learn to recognize minimal images at a human level. The roleof the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend ourunderstanding of visual recognition and its cortical mechanisms andwill enhance the capacity of computational models to learn fromvisual experience and to deal with recognition and detailedimage interpretation.</div>
},
	keywords = {Computer vision, minimal images, object recognition, visual perception, visual representations},
	issn = {1091-6490},
	doi = {10.1073/pnas.1513198113},
	url = {http://www.pnas.org/content/113/10/2744.abstract},
	author = {Shimon Ullman and Liav Assif and Eitan Fetaya and Daniel Harari}
}
