Semi-supervised Active Salient Object Detection

•We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. We then select the least confident (discriminative) samples from the unlabeled pool to form the “candidate labeled pool”.•We train a Variational Auto-Encoder (VAE) to select and add the most representative data from the “candidate labeled pool” into the labeled pool by comparing their corresponding features in the latent space. Within our frame-work, these two networks are optimized conditioned on the states of each other progressively.