Multi-head Attention
Multi-head Attention - Related Videos
08:52
The Attentional Blink
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University In order for recognition of a certain stimulus to take place, visual attention needs to be directed towards said stimulus. To the earliest parts of the visual system, objects are not objects, they are collections of visual features-lines, corners, changes in texture, color, and light. Attention is the resource that is necessary for later processing in order to recognize what a given bundle of features adds up to. This makes...
07:09
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
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Cited by 36 •
2018
Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.
09:37
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
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Cited by 5 •
2015
Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.
09:59
Visual Attention: fMRI Investigation of Object-based Attentional Control
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel— University of Southern California The human visual system is incredibly sophisticated and capable of processing large amounts of information very quickly. However, the brain's capacity to process information is not an unlimited resource. Attention, the ability to selectively process information that is relevant to current goals and to ignore information that is not, is therefore an essential part of visual perception. Some aspects of...
09:31
Touchscreen Sustained Attention Task (SAT) for Rats
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Cited by 8 •
2017
Sustained attention, or continuously monitoring situations for intermittent and unpredictable events, is a critical aspect of cognition. Here we detail how to test sustained attention in rats using touchscreen operant chambers. We demonstrate comparable performance in male and female rats, making this task useful for studying attention in both sexes.
FAQs
Each head first works with its own learned query, key, and value representation subspace. It then computes scaled dot-product attention within that subspace, producing a head-specific output. This arrangement lets the model evaluate relationships through several learned representational views rather than forcing every dependency into one shared attention operation.
Using multiple heads matters because different heads can capture different dependencies at the same time. A single attention operation provides one attention computation, whereas Multi-head Attention distributes the queries, keys, and values across learned subspaces and processes those views in parallel. The result is richer contextual and interaction modeling.
After the heads finish their parallel computations, their outputs are concatenated and passed through a linear projection. Concatenation brings the separate subspace results together, while the projection converts that combined representation into the form used by the surrounding neural network. This final step integrates head-specific information rather than leaving it separated.
An engineering implementation begins with query, key, and value representations, divides them into multiple learned subspaces, and computes scaled dot-product attention separately for each head. The resulting head outputs are then concatenated and transformed with a linear projection. This workflow preserves parallel processing while producing one integrated representation for subsequent model operations.
Engineers apply Multi-head Attention across several system types, including language processing, computer vision, time-series analysis, and recommendation systems. It also serves as a component in transformer-based architectures. These uses reflect the mechanism’s ability to represent context, interactions, and long-range relationships in sequential or structured data.
Its operation is not limited to language because the mechanism can model relationships among elements in sequential or structured data more broadly. This makes it relevant to engineering systems that analyze visual inputs, time-dependent measurements, recommendations, or other organized information where context and interactions extend across multiple elements.