IEEE transactions on neural networks and learning systems
Jan 21, 2015
Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to ef...
Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-t...
Number sense, the ability to rapidly estimate object quantities in a visual scene without precise counting, is a crucial cognitive capacity found in humans and many other animals. Recent studies have identified artificial neurons tuned to numbers of ...
Traditional models of reading lack a realistic simulation of the early visual processing stages, taking input in the form of letter banks and predefined line segments, making them unsuitable for modeling early brain responses. We used variations of t...
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational mod...
Deep convolutional neural networks (DCNNs) have attained human-level performance for object categorization and exhibited representation alignment between network layers and brain regions. Does such representation alignment naturally extend to other v...
Deep convolutional neural networks (DCNNs) have demonstrated impressive robustness to recognize objects under transformations (e.g., blur or noise) when these transformations are included in the training set. A hypothesis to explain such robustness i...
Deep neural network (DNN) models realize human-equivalent performance in tasks such as object recognition. Recent developments in the field have enabled testing the hierarchical similarity of object representation between the human brain and DNNs. Ho...
Proceedings of the National Academy of Sciences of the United States of America
Feb 23, 2021
Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition ...
Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which t...
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