Abstract: A computer-implemented unsupervised learning method of training a video feature extractor. The video feature extractor is configured to extract a feature representation from a video sequence. The method uses training data representing multiple training video sequences. From a training video sequence of the multiple training video sequences, a current subsequence; a preceding subsequence preceding the current subsequence; and a succeeding subsequence succeeding the current subsequence are selected. The video feature extractor is applied to the current subsequence to extract a current feature representation of the current subsequence. A training signal is derived from a joint predictability of the preceding and succeeding subsequences given the current feature representation. The parameters of the video feature extractor are updated based on the training signal.

Abstract: A method for transforming a frame sequence of video frames into a scene sequence of scenes. In the method: features are extracted from each video frame, and are transformed into a feature representation in a first working space; a feature interaction of each feature representation with the other feature representations is ascertained, characterizing a frame prediction; the class belonging to each already-ascertained scene is transformed into a scene representation in a second working space; a scene interaction of a scene representation with each of all the other scene representations is ascertained; a scene-feature interaction of each scene interaction with each feature interaction is ascertained; and from the scene-feature interactions, at least the class of the next scene in the scene sequence that is most plausible in view of the frame sequence and the already-ascertained scenes is ascertained.

Abstract: A method for coding a predefined time sequence of video images in a representation which is evaluable by machine made up of stationary features and nonstationary features. In the method: at least one function parameterized using trainable parameters is provided, which maps sequences of video images on representations; from the sequence of video images, N adjoining, nonoverlapping short extracts and one long extract, which contains all N short extracts are selected; using the parameterized function, a representation of the long extract and multiple representations of the short extracts are ascertained; the parameterized function is assessed; the parameters of the function are optimized with the goal that the assessment of the cost function for representations ascertained in future is expected to improve; using the function parameterized by the finished optimized parameters, the predefined time sequence of video images is mapped on the sought representation.

Abstract: A method for training an encoder that maps data samples of measurement data onto machine-evaluable representations. In the method, a set of training samples is provided, a relation being defined, in the context of a specified application, concerning the degree to which two samples are similar to one another. A function is provided that is parameterized with trainable parameters and that maps samples onto representations. A similarity measure is provided that assigns samples a similarity of representations and/or of processing products of these representations. From the set of training samples, at least one query sample is drawn. For this query sample, the following are ascertained: a set, ordered in a ranked order, of positive samples from the set that are similar to the query sample, and a set of negative samples from the set that are no longer similar to the query sample. At least the parameters are optimized.

Abstract: A computer-implemented unsupervised learning method of training a video feature extractor. The video feature extractor is configured to extract a feature representation from a video sequence. The method uses training data representing multiple training video sequences. From a training video sequence of the multiple training video sequences, a current subsequence; a preceding subsequence preceding the current subsequence; and a succeeding subsequence succeeding the current subsequence are selected. The video feature extractor is applied to the current subsequence to extract a current feature representation of the current subsequence. A training signal is derived from a joint predictability of the preceding and succeeding subsequences given the current feature representation. The parameters of the video feature extractor are updated based on the training signal.