ResNet-18

The neural mechanisms of semantic processing are difficult to delineate from lexical, acoustic, and speech-motor neural activity since most relevant neuropsychological tests require language. Furthermore, distinct signal properties of different cortical semantic processing regions may require a more comprehensive analysis of neural signal components than standard (BOLD, high gamma) metrics. We developed a Visual-based Semantic Association Task (ViSAT), evolved from predecessors (Pyramids & Palm Trees test; Camels & Cactus Test) and scaled to 100 unique trials using real-life color pict

We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed approach is based on the ideas of attention-driven visual processing and saccades, miniature eye movements influenced by attention. We conduct experiments by analyzing: i) the robustness of different deep neural network (DNN) feature extractors to partially-sensed images for image classification and object detection, and ii) the utility of saccades in masking image patches for image classification and object tracking.