A method of identifying digital objects portrayed within a digital visual media using scale variant deep learning, the method comprises: a step for training a scale-diverse segmentation neural network to analyze training indicators corresponding to training digital images and generate object segmentation outputs corresponding to different scales; receiving a digital image 700 and a user indicator 702 (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) comprising one or more pixels of the digital image; a step for utilizing the scale-diverse segmentation neural network 706 to generate a plurality of object segmentation outputs 708, 710 corresponding to a plurality of scales based on the digital image and the user indicator; and providing the plurality of object segmentation outputs for display. A system of training a scale-diverse segmentation neural network, using a training image which includes a training object and one or more training indicators corresponding to the training object. The neural network generating a predicted object segmentation output at a first scale, modifying tuneable parameters of the neural network based on comparison of outputs (as shown in fig.3).
Identifying target objects using scale-diverse segmentation neural networks