Abstract: A server receives, from a client device, an image search query comprising multiple search components. The server maps each of the multiple search components to a component embedding vector to generate multiple component embedding vectors. The server mathematically combines the multiple component embedding vectors to generate multiple query vectors with different mathematical combinations. The server identifies, for each of the different mathematical combinations, from a vector database storing representations of images, a set of image search results based on a similarity score of a query vector of the multiple query vectors and an image embedding vector for an image of the images. The server transmits, to the client device, a portion of the set of image search results.

Abstract: For example, some methods for evaluation of classifier using sum estimation may include accessing at least a part of a corpus of vectors that has been partitioned into vector datasets of different sizes, wherein the corpus of vectors has an order based on an inner product of a vector from the corpus of vectors with a vector of classifier parameters; forming a sample of the corpus of vectors by selecting, from at least two of the vector datasets, up to K vectors that are closest to an end of the order within their respective vector dataset, wherein K is an integer; identifying vectors in the sample satisfying a condition; and determining an estimate of a count of vectors satisfying the condition in the corpus of vectors based on a weighted count of the identified vectors in the sample.

Abstract: A computer obtains, from a data repository, a set of frames of a video. The computer generates, by a neural engine, a data structure of representation costs for the set of frames. The computer determines, by a dynamic programming engine and based on the data structure and at least one stored rule, a set of keyframes for the video, wherein each keyframe represents a time contiguous subset of the set of frames. The computer provides an output of the set of keyframes.

Abstract: A computer obtains a set of frames of a video. The computer generates, by a neural engine, a data structure of representation costs for the set of frames. The computer determines, by a dynamic programming engine and based on the data structure and at least one constraint, a set of summary frames for the video, wherein each summary frame represents a time contiguous subset of the set of frames. The computer provides an output of the set of summary frames.

Abstract: A server receives, from a client device, an image search query comprising a plurality of search components. The server generates a plurality of component embedding vectors based on at least a subset of the plurality of search components. The server generates a plurality of query vectors that represent different combinations of two or more of the plurality of component embedding vectors. The server identifies, by accessing a vector database storing image embedding vectors for images, one or more images based on a comparison of the image embedding vectors in the vector database with at least one of the plurality of query vectors. The server transmits, to the client device, information to cause a display, at the client device, of the one or more images in an order determined based on the comparison.

Abstract: A server obtains a set of images from a data repository. The server receives an input representing a first subset of images from the set of images that meet classification criteria. The server identifies a second subset of images from the set of images that do not meet the classification criteria. The server trains an image classification engine to classify images according to the classification criteria. The server predicts tags for an additional subset of images from the data repository. The server generates a matrix. A first dimension of the matrix specifies an image from the additional subset. A second dimension of the matrix specifies a tag from the tags. At least one cell of the matrix represents whether the image of the first dimension corresponding to the at least one cell is associated with the tag of the second dimension corresponding to the at least one cell.