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During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text.
Attention or focus, is the concentration of awareness on some phenomenon to the exclusion of other stimuli. [1] ... for example, in human vision, ...
For example, the cocktail party effect influenced researchers to look further than physical selection features, to semantic selecting features. The cocktail party effect is an example of how unattended information can gain one's attention. [26]
Attention can be guided by top-down processing or via bottom up processing. Posner's model of attention includes a posterior attentional system involved in the disengagement of stimuli via the parietal cortex, the shifting of attention via the superior colliculus and the engagement of a new target via the pulvinar. The anterior attentional ...
The review argues that perceptual load theory has been misconstrued as a hybrid solution to the early selection versus late selection debate, and that it is instead an early selection model: selection occurs because attention is necessary for semantic processing, and the difference between high-load and low-load conditions is a result of the ...
Feature integration theory is a theory of attention developed in 1980 by Anne Treisman and Garry Gelade that suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing.
This is shown, for example, in the phenomenon of 'sticky fixation', whereby infants are incapable of disengaging their attention from a particularly salient target. [10] Other research has suggested, however, that even very young infants do have some capacity to exercise control over their allocation of attention, albeit in a much more limited ...
Visual spatial attention is a form of visual attention that involves directing attention to a location in space. Similar to its temporal counterpart visual temporal attention , these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation [ 1 ] [ 2 ...