Abstract: A method for controlling a device to manage a visual scene graph model can include obtaining, via a processor in the device, a first dataset; obtaining, via the processor, a second data set different from the first dataset, the second dataset including one or more of a causal relation or an intention relation; combining, via the processor, the first dataset and the second dataset to generate a combined dataset. Also, the method can further include applying a knowledge embedding function to the combined dataset to generate learned common sense knowledge embeddings; and training a visual scene graph model based on the learned common sense knowledge embeddings to generate a trained visual scene graph model. Further, the method can include executing a function based on an output of the trained visual scene graph model. The device can include at least one of a smart television, a mobile phone or a robot.

Abstract: A method for controlling a device to manage a visual saliency model can include receiving, via a processor in the device, a center bias map and a saliency density ground-truth map for an image; normalizing values of the saliency density ground-truth map to generate a normalized density ground-truth map; and comparing values of the normalized density ground-truth map to a predefined threshold value to generate an enhanced ground-truth map. The method can further include subtracting the enhanced ground-truth map from the center bias map to generate a negative candidates map; normalizing values of the negative candidates map to generate a normalized candidates map; and performing a sampling process on the normalized candidates map to generate a negative point map. Also, the method includes applying a filter function to the negative point map to generate a negative density map.

Abstract: A neural network system, comprising: instructions for implementing at least a SWBN layer in a neural network, and wherein the instructions perform operations comprising: during training of the neural network system on a plurality of batches of training data and for each of the plurality of batches: obtaining a respective first layer output for each of the plurality of training data; determining a plurality of normalization statistics for the batch from the first layer outputs; generating a respective normalized output for each training data in the batch; updating the whitening matrix by a covariance matrix; performing stochastic whitening on the normalized components of each first layer output; transforming the whitened data for each training data; generating a respective SWBN layer output for each of the training data from the transformed whitened data for each training data in the batch; and providing the SWBN layer output.