Abstract: A content saliency network is a machine-learned neural network that predicts the saliency of elements of a content item. The content saliency network may be used in a method that includes determining a set of elements in draft content and computing a first pixel-level vector for the content. The method may also include, for each element in the set of elements, computing a vector of simple features for the element, the simple features being computed from attributes of the element, computing a second pixel-level vector for the element, computing a third pixel-level vector for an intermediate context of the element, and providing the vectors to the content saliency network. The content saliency network provides a saliency score for the element. The method further includes generating an element-level saliency map of the content using the respective saliency scores for the set of elements and providing the saliency map to a requestor.

Abstract: A content saliency network is a machine-learned neural network that predicts the saliency of elements of a content item. The content saliency network may be used in a method that includes determining a set of elements in a UI and computing a first context vector for the content. The method may also include, for each element in the set of elements, computing a vector of simple features for the element, the simple features being computed from attributes of the element, computing a second context vector for the element, computing a third context vector for an intermediate context of the element, and providing the vectors to the content saliency network. The content saliency network provides a saliency score for the element. The method further includes generating an element-level saliency map of the content using the respective saliency scores for the set of elements and providing the saliency map to a requestor.

Abstract: A content saliency network is a machine-learned neural network that predicts the saliency of elements of a content item. The content saliency network may be used in a method that includes determining a set of elements in a UI and computing a first context vector for the content. The method may also include, for each element in the set of elements, computing a vector of simple features for the element, the simple features being computed from attributes of the element, computing a second context vector for the element, computing a third context vector for an intermediate context of the element, and providing the vectors to the content saliency network. The content saliency network provides a saliency score for the element. The method further includes generating an element-level saliency map of the content using the respective saliency scores for the set of elements and providing the saliency map to a requestor.

Abstract: A content saliency network is a machine-learned neural network that predicts the saliency of elements of a content item. The content saliency network may be used in a method that includes determining a set of elements in draft content and computing a first pixel-level vector for the content. The method may also include, for each element in the set of elements, computing a vector of simple features for the element, the simple features being computed from attributes of the element, computing a second pixel-level vector for the element, computing a third pixel-level vector for an intermediate context of the element, and providing the vectors to the content saliency network. The content saliency network provides a saliency score for the element. The method further includes generating an element-level saliency map of the content using the respective saliency scores for the set of elements and providing the saliency map to a requestor.