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C5W3L07 Attention Model Intuition

Skip to Main Content. First published: 20 March The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications including object recognition, unmanned vehicle navigation, and image and video coding and processing.

In this book, the authors provide an up to date and highly applied introduction to the topic of visual attention, aiding researchers in creating powerful computer vision systems. Areas covered include the significance of vision research, psychology and computer vision, existing computational visual attention models, and the authors' contributions on visual attention models, and applications in various image and video processing tasks.

Provides a key knowledge boost to developers of image processing applications Is unique in emphasizing the practical utility of attention mechanisms Includes a number of real-world examples that readers can implement in their own work: robot navigation and object selection image and video quality assessment image and video coding Provides codes for users to apply in practical attentional models and mechanisms.

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Deep learning

Furthermore, the 3rd layer of the network better predicted V4 cell activity while the 4th and final layer better predicted IT. Indicating a correspondence between model layers and brain areas. Another finding was that networks that performed better on object recognition also performed better on capturing IT activity, without a need to be directly optimized on IT data. This trend has largely held true for larger and better networks, up to some limits see Q Another paper, Khaligh-Razavi and Kriegeskorte , also uses representational similarity analysis to compare 37 different models to human and monkey IT.

They too found that models better at object recognition better matched IT representations. The neocognitron model mentioned in Q2 was inspired by the findings of Hubel and Wiesel and went on to inspire modern CNNs, but it also spawned a branch of research in visual neuroscience recognized perhaps most visibly in the labs of Tomaso Poggio , Thomas Serre , Maximilian Riesenhuber , and Jim DiCarlo , among others.

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Models based on stacks of convolutions and max-pooling were used to explain various properties of the visual system. The path taken by visual neuroscientists and computer vision researchers has variously merged and diverged, as they pursued separate but related goals. But in total, CNNs can readily be viewed as a continuation of the modeling trajectory set upon by visual neuroscientists.

The contributions from the field of deep learning relate to the computational power and training methods and data that allowed these models to finally become functional.

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Convolutional neural networks have three main traits that support their use as models of biological vision: 1 they can perform visual tasks at near-human levels, 2 they do this with an architecture that replicates basic features known about the visual system, and 3 they produce activity that is directly relatable to the activity of different areas in the visual system. Features of the visual hierarchy. To start, by their very nature and architecture, they have two important components of the visual hierarchy.

First, receptive field sizes of individual units grow as we progress through the layers of the network just as they do as we progress from V1 to IT. Second, neurons respond to increasingly complex image features as we progress through the layers just as tuning goes from simple lines in V1 to object parts in IT.

Attention? Attention!

This increase in feature complexity can be seen directly through visualization techniques available in CNNs. Visualizations of what features the network learns at different layers. Looking more deeply into 3 , many studies subsequent to the original work Q4 have further established the relationship between activity in CNNs and the visual systems. These all show the same general finding: the activity of artificial networks can be related to the activity of the visual system when both are shown the same images. Furthermore, later layers in the network correspond to later areas in the ventral visual stream or later time points in the response when using methods such as MEG.

Many different methods and datasets have been used to make these points, as can be seen in the following studies amongst others : Seibert et al.

Correlation between the representations at different CNN layers and brain areas from Cichy et al. The focus of these studies is generally on the initial neural response to briefly-presented natural images of various object categories. In addition to comparing activities, we can also delve deeper into 1 , i. Detailed comparisons of the behavior of these networks to humans and animals can further serve to verify their use as a model and identify areas where progress is still needed.

Such behavioral effects have been studied in: Rajalingham et al. Whether all this meets the specification of a good model of the brain is probably best addressed by looking at what people in vision have said they wanted out of a model of the visual system:. Such computational approaches are critically important because they can provide experimentally testable hypotheses, and because instantiation of a working recognition system represents a particularly effective measure of success in understanding object recognition. Generally, no. Several studies have directly compared the ability of CNNs and previous models of the visual system such as HMAX to capture neural activity.

CNNs come out on top.

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Such studies include: Yamins et al. A reasonable definition of a mechanistic model is one in which internal parts of the model can be mapped to internal parts of the system of interest. Descriptive models, on the other hand, are only matched in their overall input-output relationship. So a descriptive model of the visual system may be one that takes in an image and outputs an object label that aligns with human labels, but does so in a way that has no obvious relation to the brain.

As described above, however, layers of a CNN can be mapped to areas of the brain. Therefore, CNNs are mechanistic models of the representational transformation carried out by the ventral system as it does object recognition. For a CNN to, as a whole, be a mechanistic model does not require that we accept that all sub-components are mechanistic. Take as an analogy, the use of rate-based neurons in traditional circuit models of the brain. Rate-based neural models are simply a function that maps input strength to output firing rate. As such, these are descriptive models of neurons: there are no internal components of the model that relate to the neural processes that lead to firing rate detailed bio-physical models such as Hodgkin-Huxley neurons would be mechanistic.

So are the components of a CNN i. This question is harder to answer. While these layers are comprised of artificial neurons which could plausibly be mapped to groups of real neurons, the implementations of many of the computations are not biological. For example, normalization in the networks that use it is implemented with a highly-parameterized divisive equation. We believe that these computations can be implemented with realistic neural mechanisms see the above-cited example network , but those are not what are at present used in these models though I, and others, are working on it… see Q For neuroscientists used to dealing with things on the cellular level, models like CNNs may feel abstracted beyond the point of usefulness cognitive scientists who have worked with abstract multi-area modeling for some time though may find them more familiar.

Relating CNNs to brain areas and processing. But even without exact biological details, we can still map components of the CNN to components of the visual system. First, inputs to a CNN are usually 3-D RGB pixel values that have been normalized or whitened in some way, roughly corresponding to computations performed by the retina and lateral geniculate nucleus.

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The convolutions create feature maps that have a spatial layout, like the retinotopy found in visual areas, which means that each artificial neuron has a spatially-restricted receptive field. The convolutional filter associated with each feature map determines the feature tuning of the neurons in that feature map. Individual artificial neurons are not meant to be mapped directly to individual real neurons; it may be more reasonable to think of individual units as cortical columns. Which layers of the CNN correspond to which brain areas? The early work using models that only contained a small number of layers provided support for a one layer to one brain area mapping.

For example, in Yamins et al. The exact relationship, however, will depend on the model used with deeper models allowing for more layers per brain area. The fully connected layers at the end of a convolutional network have a more complicated interpretation. Their close relationship to the final decision made by the classifier and the fact that they no longer have a retinotopy makes them prefrontal cortex-like.

But they also may perform well when predicting IT activity. Lots of things.