Feature attention operates in a spatially global way, with attended feature values being prioritized for selection outside the focus of attention. Accounts of global feature attention have emphasized feature competition as a determining factor. Here, we use magnetoencephalographic recordings in humans to test whether competition is critical for global feature selection to arise. Subjects perfor...

Attention to a location in a visual scene affects neuronal responses in visual cortical areas in a retinotopically specific manner. Optical imaging studies have revealed that cortical responses consist of two components of different sizes: the stimulus-nonspecific global signal and the stimulus-specific mapping signal (domain activity). It remains unclear whether either or both of these compone...

Data from brain-damaged and neurologically intact populations indicate hemispheric asymmetries in the temporo-parietal cortex for discriminating an object's global form (e.g. the overall shape of a bicycle) versus its local parts (e.g. the spokes in a bicycle tire). However, it is not yet clear whether such asymmetries reflect processes that (i) bias attention toward upcoming global versus loca...

We investigated neural substrates of global/local processing of bilateral hierarchical stimuli using functional magnetic resonance imaging (fMRI). Subjects were presented with two compound letters that were displayed simultaneously in the left and right visual fields, respectively. In a steady-state, block-design paradigm, hemodynamic responses were recorded while subjects detected infrequent g...

Remote sensing image scene classification is an important task of remote interpretation, which has recently been well addressed by the convolutional neural network owing to its powerful learning ability. However, due multiple types geographical information and redundant background images, most CNN-based methods, especially those based on a single CNN model ignoring combination global local feat...

Fully convolutional structures provide feature maps acquiring local contexts of an image by only stacking numerous layers. These are known to be effective in modern state-of-the-art object detectors such as Faster R-CNN and SSD find objects from contexts. However, the quality can further improved incorporating global when some ambiguous should identified surrounding or background. In this paper...