Speaker
Description
Since collecting pixel-level groundtruth data is expensive, unsupervised visual understanding problems are currently an active research topic. In particular, several recent methods based on generative models have achieved promising results for object segmentation and saliency detection. However, since generative models are known to be unstable and sensitive to hyperparameters, the training of these methods can be challenging and time-consuming.
In this work, we introduce an alternative, much simpler way to exploit generative models for unsupervised object segmentation. First, we explore the latent space of the BigBiGAN -- the state-of-the-art unsupervised GAN, which parameters are publicly available. We demonstrate that object saliency masks for GAN-produced images can be obtained automatically with BigBiGAN. These masks then are used to train a discriminative segmentation model. Being very simple and easy-to-reproduce, our approach provides competitive performance on common benchmarks in the unsupervised scenario.
