@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}
}
@article {2326,
	title = {Measuring and modeling the perception of natural and unconstrained gaze in humans and machines},
	year = {2016},
	month = {11/2016},
	abstract = {<p>Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing social interactions. How accurate are humans in determining the gaze direction of others in lifelike scenes, when they can move their heads and eyes freely, and what are the sources of information for the underlying perceptual processes? These questions pose a challenge from both empirical and computational perspectives, due to the complexity of the visual input in real-life situations. Here we measure empirically human accuracy in perceiving the gaze direction of others in lifelike scenes, and study computationally the sources of information and representations underlying this cognitive capacity. We show that humans perform better in face-to-face conditions compared with recorded conditions, and that this advantage is not due to the availability of input dynamics. We further show that humans are still performing well when only the eyes-region is visible, rather than the whole face. We develop a computational model, which replicates the pattern of human performance, including the finding that the eyes-region contains on its own, the required information for estimating both head orientation and direction of gaze. Consistent with neurophysiological findings on task-specific face regions in the brain, the learned computational representations reproduce perceptual effects such as the Wollaston illusion, when trained to estimate direction of gaze, but not when trained to recognize objects or faces.</p>
},
	keywords = {computational evaluation, computational modeling, Computer vision, empirical evaluation, estimation of gaze direction, Gaze perception, joint attention, Machine Learning},
	author = {Daniel Harari and Tao Gao and Nancy Kanwisher and Joshua B. Tenenbaum and Shimon Ullman}
}
