Abstract: A neural model for representing an input sequence over a vocabulary in a ranker of a neural information retrieval model. An input sequence is embedded based at least on the vocabulary. An importance of each token over the vocabulary is predicted with respect to each token of the embedded input sequence. A predicted term importance of the input sequence over the vocabulary is determined by performing an activation over the embedded input sequence.

Abstract: Information retrieval methods employ a neural network encoder configured to receive a dense representation and generate a composite code comprising C clusters of dimension L from the dense representation. An activation function is configured to generate a sparse composite code from the composite code. The sparse composite code comprises a binary representation. An index can be generated using the sparse composite code.

Abstract: A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.

Abstract: A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.