Abstract: Binary classification models can be trained to classify data as being in one of two classes. Membership in a class may be imbalanced so that there are more members in one class than the other. Additionally, one of the classes may have a higher importance than the other, yet appear much less frequently. It is possible to train the binary classification model using a base loss function and a regularization function based on a ranking of training results in order to reduce the false positives at a high true positive rate.

Abstract: A computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators—three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.

Abstract: A computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators—three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.

Abstract: A system and method are provided for object tracking in a sequence of images of a scene acquired by an imaging device. The method includes generating a representation of an object and its surroundings; generating a tracking score based on the representation of the object and its surroundings, and a classification scheme; detecting a peak in the tracking score corresponding to a location of the object in the image; and adjusting the representation of the object and its surroundings and the classification scheme according to one or more new appearances of the object.

Abstract: A system and method are provided for object tracking in a sequence of images of a scene acquired by an imaging device. The method includes generating a representation of an object and its surroundings; generating a tracking score based on the representation of the object and its surroundings, and a classification scheme; detecting a peak in the tracking score corresponding to a location of the object in the image; and adjusting the representation of the object and its surroundings and the classification scheme according to one or more new appearances of the object.