This disclosure relates to improved techniques for performing computer vision functions including common object counting and instance segmentation. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can be trained using image-level supervision techniques that utilize a loss function to jointly train an image classification branch and a density branch of the neural network architecture. The neural network architecture constructs per-category density maps that can be used to generate analysis information comprising global object counts and locations of objects in images.
OBJECT COUNTING AND INSTANCE SEGMENTATION USING NEURAL NETWORK ARCHITECTURES WITH IMAGE-LEVEL SUPERVISION