Systems, methods, and computer-readable media are disclosed for segmenting objects in digital visual media utilizing one or potentially, more salient content neural networks. A mobile device, such as a mobile or cell phone or tablet 300, captures real-time images 302 and applies a real-time neural network 304 embedded in the mobile device, to train a network to predict foreground pixels from a captured training image, comparing the predicted images with ground truth pictures. The trained network is then used identify objects in real-time digital media 306 and generate a modified image for display, based on said identified features 308. The digital image may be part of real-time digital media feed. A second neural network may be present on the mobile device, using training data which is different from the first training image. Background pixels may be separated from foreground ones, and both background and foreground pixels may be modified. A system where the mobile device acts as a client, while the neural network operates on the server, is also envisaged (Figure 1).
Deep salient content neural networks for efficient digital object segmentation