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When these features become highly familiar, they may start to guide attention in automatic fashion [38], [39] even though the visual features associated with foods would not be visually highly salient (c.f. saliency data from Experiment 1).
Then, the visual features are extracted from convolutional layers.
Afterwards, the visual features and multimodal cloud features are integrated.
A local descriptor uses the visual features of regions or objects to describe the image, where as the global descriptor uses the visual features of the whole image.
In the protocol learning, larger weights are given to the visual features that are spatiotemporally consistent.
This shows the strength of both the radar sensor and of the visual features.
Note that "+M" indicates concatenating the visual features with multimodal information.
Afterwards, we employ a weighted strategy to integrate the visual features with multimodal features.
In the synthesis stage, HMMI is used to estimate the visual features from speech data.
In both cases, a 180° difference in course is easily overcome by the visual features.
Therefore, the visual features of building elements has been exploited here.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com